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Official MiniMax Model Context Protocol (MCP) server that enables interaction with powerful Text to Speech, image generation and video generation APIs.

Model Context Protocol server that integrates AgentQL's data extraction capabilities.

HyperChat is a Chat client that strives for openness, utilizing APIs from various LLMs to achieve the best Chat experience, as well as implementing productivity tools through the MCP protocol.

Official Firecrawl MCP Server - Adds powerful web scraping to Cursor, Claude and any other LLM clients.

5ire is a cross-platform desktop AI assistant, MCP client. It compatible with major service providers, supports local knowledge base and tools via model context protocol servers .

Code at the speed of thought – Zed is a high-performance, multiplayer code editor from the creators of Atom and Tree-sitter.

Official remote MCP server for Framesail AI. Create long-form (faceless YouTube) videos end to end from any MCP client: script, locked character references, storyboard, voiceover, and final video editing — with characters and style held consistent across every shot. Making long-form AI video today means 8+ tabs stitched by hand — an LLM for the script, a voice model, an image model, a video model — with characters drifting between tools and style resetting at every export. Framesail replaces the patchwork: the whole pipeline runs in one place and manages your video's context end to end. Six stages: Style (paste images, videos, or YouTube links and Framesail reverse-engineers the look, voice, and direction), Script (write it yourself or generate it in your narrative style), Reference images (auto-generated for every character, place, and prop), Voiceover (one narrator or many characters, with word-level timing), Storyboard (planned scene by scene), and Editor (captions, music, SFX, then export). No black box: you control every prompt, asset, model, and setting.

ContextBridge (CB) is a local-first retrieval layer that sits between your codebase and your AI coding agent. Instead of pasting raw source files into context or letting the AI guess which files matter, CB indexes your codebase's real structure (via Graphify) and returns a compact, ranked result — owner file, related files, key symbols, and dependency chains — grounded in your actual code. A typical response is ~4–8 KB instead of the 100+ KB of raw source an AI would otherwise need to read to answer the same question — roughly a 96% cut in input tokens sent to your cloud AI, with zero hallucinated file paths or method names.

Local-first code retrieval for AI agents — cuts codebase context from tens of thousands of tokens to a few hundred, with zero hallucinated file paths.

Connect AI assistants to AppAmbit — the command center for your mobile & desktop apps. Query real-time analytics, sessions, and crash reports; read and push remote config; send push notifications, provision and query managed per-app SQLite databases, deploy serverless Cloud Code functions; and manage a headless CMS. Also generates SDK setup snippets and runs integration diagnostics. Supports .NET MAUI, Swift, Objective-C, Android and more. Built for indie devs, mobile teams, and agencies.

Read-only MCP server that lets your coding agent see the sibling repositories next to the one it's working in overview, search, read, zero write tools. Security by architecture: path traversal, symlink escapes and secret files are blocked, proven by tests.

Automated context collection for coding agents. Mines your real Claude Code & Cursor sessions for repeated instructions and corrections, and distills them into CLAUDE.md / AGENTS.md rules you approve — with your own past messages as evidence. 100% local, zero dependencies, no API key. Includes global mode (cross-project rules about how you work), staleness detection for CI, and a Claude Code plugin. Website: https://thecontextlayer.ai

<a href="https://modelrunner.ai">ModelRunner</a> is a hosted remote MCP server that lets AI assistants like Claude and Cursor run 100+ AI models — text-to-image, image-to-image, text-to-video, image-to-video, video-to-video, music generation, speech-to-text, and image-to-3D, including Kling, HiDream, Hunyuan Image, Stable Audio, and Rodin. One connection exposes every model as a callable tool: search the catalog, inspect a model's input schema, run inference with run_model, and get results back as hosted URLs directly in the conversation. Unlike single-vendor MCP servers, ModelRunner aggregates models from multiple providers behind one endpoint and one billing account — pay-per-use credits, no subscription. Authorization is OAuth 2.1: your assistant opens a browser window once; no API key is ever pasted into a config file. Beyond inference, the server can upload local files for use as model inputs and author reusable "wrappers" — simplified, purpose-built interfaces over base models. Endpoint: https://mcp.modelrunner.run/mcp (Streamable HTTP; registry name ai.modelrunner/mcp) Setup and full tool reference: <a href="https://modelrunner.ai/mcp">ModelRunner MCP setup guide</a> · <a href="https://github.com/modelrunner/modelrunner-mcp">GitHub</a>

MDMA (Markdown Document with Mounted Applications) MCP server. Exposes the MDMA spec, authoring prompts, package metadata, and always-fresh docs so AI assistants generate interactive Markdown instead of plain text — forms, tables, approval gates, charts, callouts, webhooks and more, from a validated schema. Tools: get-spec, get-prompt, build-system-prompt, validate-prompt, list-packages, list-docs, get-doc. Use it when building chat apps, agents, or tools that emit MDMA documents. Open source (MIT). Install: npx @mobile-reality/mdma-mcp Project: https://mdma.software Repo: https://github.com/MobileReality/mdma Built by Mobile Reality — https://themobilereality.com

Official MCP server for APIFreaks. Real-time weather, IP, WHOIS, DNS, SSL, currency, screenshots, and more for any AI agent.

Generate real estate listing videos from Claude, Cursor, and any MCP client — AI virtual staging, twilight photo edits, drone-style motion clips, voiceover, and rendered property videos for Instagram, TikTok, and YouTube. Official Reel Estate MCP server.

Security scanner for MCP servers — vet an MCP before you wire it into an agent. Detects prompt-injection, credential exfiltration (via taint analysis), RCE, and supply-chain risks, and catches cross-server exfil chains no single server reveals. Zero-dependency local CLI, SARIF output, CI-gateable, no account.

An MCP server that gives any LLM or agent clean YouTube transcripts on demand: a single video, a whole channel, or a playlist, plus AI cleanup of auto-generated captions. API-key auth, credit-based, same backend as the public v1 API. Get a free API key with 25 free credits at youtubetranscriptdownload.com/account.

AI agents propose database schema and data changes as reviewable requests before anything is applied — no direct write access. Works with Claude Code, Cursor, and any MCP client.

Use Paste clipboard history as context in AI tools. Find copied notes, links, files, and screenshots, work with saved items, and organize useful context into pinboards. Runs locally on your Mac through Paste's built-in MCP server.

MCP server that lets LLM agents play real PICO-8 carts in deterministic lockstep: press buttons, step frames, inspect screenshots, read Lua globals, peek memory, reset/replay, and watch live in a browser.

Tubask — hosted YouTube MCP for Claude & Cursor ·

Structured project memory for AI agents, delivered through a project-scoped remote MCP connector.

Kurdish (Sorani & Kurmanji) text-to-speech and speech-to-text for AI agents — 664 AI voices, audio transcription with dialect detection, and live streaming STT. Remote streamable-HTTP MCP server; requires a free kurdishtts.com API key. Listed in the official MCP registry as com.kurdishtts/kurdish-tts-stt.

Gives AI agents verifiable, tamper-evident receipts for their actions — attest_action signs a cryptographic receipt for what the agent did (email sent, payment made, form filed), verify_receipt checks it offline, get_identity returns the agent's did:key. Sign locally, verify anywhere, zero backend.

What this is A sovereign, encrypted, sharable, persistent memory protocol for AI agents. A Model Context Protocol server that exposes eight tools any MCP-capable AI agent (Claude Code, Claude Desktop, custom agents) can call to gain a persistent, encrypted memory layer the user owns: saihm_remember — store an encrypted memory cell saihm_recall — retrieve and decrypt your memories saihm_forget — true cryptographic erasure (GDPR Art. 17) saihm_status — your protocol-runtime stats and storage tier dashboard saihm_share / saihm_revoke_share — selectively share a memory with another agent or user saihm_governance_propose / saihm_governance_vote — protocol governance via gSAIHM Each tool forwards to a SAIHM operator endpoint that runs the full protocol stack on COTI V2 mainnet. The server itself holds no crypto, no storage, and no protocol runtime — those live behind the operator endpoint. Tool reference Tool Title Behavior saihm_remember Remember writes a new memory cell saihm_recall Recall read-only; safe to repeat saihm_forget Forget (GDPR erasure) destructive — irreversible erasure saihm_status Status read-only saihm_share Share writes a sharing contract saihm_revoke_share Revoke share withdraws a grant saihm_governance_propose Propose (governance) opens a proposal saihm_governance_vote Vote (governance) casts a vote Each tool carries MCP annotations (readOnlyHint, destructiveHint, idempotentHint, openWorldHint) and a human-readable title, so MCP hosts can gate confirmations and agents can select the right tool at reasoning time. Companion package This package speaks MCP. For production client-side cryptography — post-quantum sealing, authenticated sharing, and provable erasure performed on your own machine so the operator stays blind — pair it with @saihm/client-pro. See it run Runnable, one-command demos ground a memory you own in every major model — Claude, GPT, DeepSeek, Qwen, Kimi, GLM — then prove you can erase it, alongside drop-in adapters for LangChain, LlamaIndex, CrewAI, AutoGen, and LangGraph. Each runs offline in about a minute; no account needed. Live demos: https://citw2.github.io/saihm-demos/ demo-claude-code wires this server into Claude Code and Cursor as an MCP server. Measured — up to ~86% fewer context tokens. Most agents re-send their entire transcript every turn, so context spend grows ~O(N²) over a session; recalling a bounded set of memory cells instead cut input tokens by 62.8%–85.9% across a realistic multi-session coding task. The benchmark is open, offline, and deterministic — reproduce the number rather than trust it: git clone https://github.com/citw2/saihm-token-benchmark cd saihm-token-benchmark && npm install && node benchmark.mjs Install npm install @saihm/mcp-server # or run directly without install: npx @saihm/mcp-server Configure The server needs two env vars: SAIHM_ENDPOINT_URL=https://operator.example.com/mcp SAIHM_AUTH_HEADER=Bearer <token-issued-by-your-operator> SAIHM_ENDPOINT_URL — the SAIHM operator endpoint. Operators publish their endpoint URLs at https://saihm.coti.global. SAIHM_AUTH_HEADER — the Authorization header value the operator expects (typically a Bearer <token> issued to you after key-bound enrolment). The server is authentication-agnostic and never transmits raw private keys; the operator's enrolment flow keeps your signing key on your machine. Place these in a .env file alongside the server (the .gitignore excludes all .env* files from any future repo). Wire into Claude Code { "mcpServers": { "saihm": { "command": "npx", "args": ["@saihm/mcp-server"], "env": { "SAIHM_ENDPOINT_URL": "https://operator.example.com/mcp", "SAIHM_AUTH_HEADER": "Bearer <token>" } } } } What gets persisted, where The server itself persists nothing. The operator endpoint runs the full protocol stack: cells are encrypted under a per-cell DEK, sealed by a per-agent KEK, persisted to the operator's configured durable storage, and audited on COTI V2 mainnet. See the operator's documentation for tier details, and Storage is the operator's responsibility (by design) below. Storage is the operator's responsibility (by design) For operators — read this first. SAIHM does not hard-wire your durable storage to any single provider, and it does not silently provision storage for you. Choosing and configuring where cells are persisted is your job, on purpose. This is a deliberate design choice for operator convenience and data sovereignty — not a missing feature. If memory writes fail with a storage error, it almost always means the backend has not been configured yet. Why it works this way: Provider sovereignty. You decide where your tenants' encrypted cells live. The protocol never locks you to one vendor or one network. Local-first, then deep-archive. A typical operator routes writes to a local IPFS (Kubo) node first — fast, authoritative, and under your own control — and then asynchronously to a Filecoin deep-archive provider such as Pinata, Synapse, or Lighthouse. The same content addressing spans both tiers. Your memory and your tenants' take the same path. Whatever backend you configure serves both the operator's own memory and every tenant's — there is no separate hidden sink hard-coded to one provider. What you configure (your operator deployment guide lists the exact settings): a reachable IPFS / Kubo endpoint (a local node is recommended) for the authoritative low-latency tier, and credentials for at least one Filecoin / IPFS pinning provider for durable deep-archive. If neither is configured, the endpoint has nowhere durable to put cells and will reject writes rather than lose data. That refusal is intentional. Prefer not to run storage yourself? Join SAIHM. You have two paths, and either is fine: Run your own operator endpoint and configure the storage backend as described above — full sovereignty, your infrastructure. Join the hosted SAIHM operator and let it provide durable storage for you. It runs blind / non-custodial: paired with client-side sealing (see @saihm/client-pro and @saihm/mcp-server-pro), it only ever stores ciphertext and never holds your keys — so you get managed storage without giving up custody. Enrol via Join SAIHM at https://saihm.coti.global (a paid hosted service). Reporting engine A reporting library is bundled as a sub-export, so operators can compose the eight MCP calls into bespoke reports with their own tooling (no extra dependency, no extra service): import { validateBespokeTemplate, registerTemplate, generateRegistryAttestation, StubPublicRegistry, InMemoryReportingRuntime, GDPR_ART15_FIELDS, REGISTRY_ATTESTATION_FIELDS, type BespokeReportTemplate, } from "@saihm/mcp-server/reporting"; What it covers Field universe (FIELD_UNIVERSE) — 280 fields (262 framework + 18 ledger). Templates that project a field outside this set are rejected at validation. Bespoke template schema — zod validator + universe-membership check + scope/cap enforcement. Authorization path validators — 4 paths: public / self / operator-self / operator-for-downstream. Receipt emission — 6 sub-kinds (report_generated / report_rejected / template_registered / template_superseded / erasure_chain_broken / rate_limit_exceeded) under a stable, domain-separated receipt namespace. Framework smoke — registry-attestation (public auth) for end-to-end plumbing verification. Constraints Every fieldProjections[] entry MUST be in FIELD_UNIVERSE. scope.customerIdHashes 64-hex; max 10,000 per template. scope.timeRange window ≤ 366 days. fieldProjections length 1–200. framework ∈ {gdpr-art-15, gdpr-art-17, soc2-t1, soc2-t2, iso27001, aml, audit-export, billing-history, registry-attestation}. format ∈ {pdfa3, json, csv}. Worked example const template: BespokeReportTemplate = { templateId: "acme-q1-summary", templateVersion: 1, operatorIdHash: "ab".repeat(32), scope: { customerIdHashes: ["cd".repeat(32)], timeRange: { from: "2026-01-01T00:00:00Z", to: "2026-04-01T00:00:00Z" }, }, framework: "gdpr-art-15", fieldProjections: [GDPR_ART15_FIELDS[0], GDPR_ART15_FIELDS[1]], format: "pdfa3", }; const v = validateBespokeTemplate(template); if (!v.valid) throw new Error(v.errors.join(", ")); const runtime = new InMemoryReportingRuntime(); // replace with your audit-ledger runtime const reg = await registerTemplate(template, runtime); if (reg.ok) console.log("registered:", reg.templateHash); In production, replace InMemoryReportingRuntime with a runtime that persists audit payloads to your operator's audit ledger. Operators who inject signature verifiers should use pure-crypto libraries (@noble/curves for EIP-712, @noble/post-quantum for FIPS 204 ML-DSA) — the package itself bundles no EVM tooling. Security The server enforces a small set of defaults so misconfiguration cannot leak the Authorization header in transit: HTTPS-only endpoints. SAIHM_ENDPOINT_URL must use https://. Plain http:// is rejected at construction time, except for 127.0.0.1 and localhost (so a local operator endpoint works during development). Per-call abort window. Each request runs under an AbortController that aborts after 30s, preventing a hung endpoint from starving the MCP server. Response-size cap. Responses whose Content-Length exceeds 16 MB are rejected before deserialisation. No header echo. Authorization is never included in thrown error messages or stdout. No filesystem reads. The package never reads from disk; configuration flows entirely through env vars. Zero EVM tooling. No ethers, no eth_*, no Solidity. If operators inject signature verifiers via AuthVerifiers, they should use pure-crypto libraries (@noble/curves, @noble/post-quantum). Trust model: this client trusts whatever endpoint the operator configures. Cell IDs, audit anchors, and report receipts returned from that endpoint are surfaced to the agent verbatim — operators are the authority for content shown via saihm_recall. Verifying receipts against COTI V2 mainnet anchors is out of scope for this server; consume the cellId and auditCellId fields and verify against your own SAIHM mainnet read path. For distribution integrity, each release carries the npm registry signature; verify with npm audit signatures (and inspect npm view @saihm/mcp-server --json | jq .dist). Dependencies The published npm package has a minimal runtime surface: Dependency License Role Node.js (≥ 20.x) MIT Runtime @modelcontextprotocol/sdk MIT MCP SDK; binds the eight-tool surface TypeScript Apache-2.0 Build-time only tsx MIT TypeScript runner for tests + CLI No copyleft, no proprietary dependencies. Cryptographic primitives at the operator-endpoint layer (ML-DSA-65 / Ed25519 / key derivation) are not bundled into this MCP server; operators implementing the protocol stack are recommended to use @noble/post-quantum and @noble/curves (MIT) rather than rolling custom code. Achievements OpenSSF Best Practices Passing badge — project 12898, 100% Passing criteria (2026-05-19). https://www.bestpractices.dev/projects/12898 IETF Independent Submission Stream — draft-saihm-memory-protocol-01 (2026-05-27) is In ISE Review in the Independent Submission Stream. It is not an Internet Standard, is not endorsed by the IETF, and has no formal standing in the IETF standards process. https://datatracker.ietf.org/doc/draft-saihm-memory-protocol/ npm registry — @saihm/mcp-server@0.3.4 published (2026-06-22) adds a conspicuous "Storage is the operator's responsibility (by design)" section — documenting BYO storage and the Join-SAIHM hosted, non-custodial option. 0.3.3 (2026-06-22) was a documentation release that states the Independent-Submission status precisely (no implied IETF endorsement) and cross-references the companion package @saihm/client-pro. 0.3.2 (2026-06-22) corrected the documented operator-endpoint path to /mcp (the canonical SAIHM_ENDPOINT_URL path) across the README and client comments. 0.3.1 (2026-05-28) was a metadata patch that sources the MCP serverInfo.version from package.json (was hardcoded "0.1.0" from 0.1.0 through 0.3.0). 0.3.0 (also 2026-05-28) aligned the saihm_status response shape with draft-saihm-memory-protocol-01 §3.4 (full eight-field schema: prs, bfsi, bfsi_window_start_ts, bfsi_R, bfsi_M, shards, contracts, governance). 0.2.0 (also 2026-05-28) aligned the cell-tuple response shape with §2.1; 0.1.3 was the OpenSSF Best Practices Passing badge release (2026-05-19). MCP Registry / Glama — server listed for discovery (2026-05-16). Roadmap A 12-month roadmap is maintained in the project's AAIF proposal and will be mirrored to https://saihm.coti.global/roadmap with the v0.2.x release. Near-term tracks: 2026-Q2 — Operator-endpoint reference implementation; OpenSSF Silver pursuit (governance, code-of-conduct, DCO, signed releases, coverage tooling, assurance case). 2026-Q3 — First 2–3 external organization deployments; formal AAIF Project Proposal submission when adoption blockers clear. 2026-Q4 — NIST AI RMF crosswalk public review; EU AI Act compliance-checklist generator. OpenSSF Silver award (target). 2027-Q1 — Independent-stream (ISE) RFC publication, subject to RFC-Editor review — not an IETF-consensus standard; v1.0 reference implementation. License Apache-2.0 — see LICENSE. Project Site: https://saihm.coti.global Issue tracker: https://github.com/SAIHM-Admin/saihm-mcp/issues Security: see SECURITY.md for private vulnerability disclosure Contributing: see CONTRIBUTING.md and CODE_OF_CONDUCT.md Governance: see GOVERNANCE.md Changelog: see CHANGELOG.md

A private, open-source AI-text checker, as a browser app and an MCP server, all on your own machine.

devops-mcp lets AI assistants (Claude Desktop, Cursor, Windsurf) connect, scan, plan, and deploy on real Linux servers over SSH — without handing the model the keys to the kingdom. Reading is always allowed; anything that changes state on a production-like server is refused unless the user supplies a secret elevation token the model never sees. Three time-limited modes (SAFE / PROVISION / FULL) with auto-expiry, a production write-gate that demands backup confirmation for irrecoverable ops, shell-quoted arguments, prompt-injection-tagged output, and a full JSON-lines audit log. Self-hosted: clone and build the project locally — an online/hosted MCP is not safe for this security tooling. 32 tools, TypeScript, MIT.

devops-mcp lets AI assistants (Claude Desktop, Cursor, Windsurf) connect, scan, plan, and deploy on real Linux servers — without handing the model the keys to the kingdom. Reading is always allowed; anything that changes state on a production-like server is refused unless the user pastes a secret elevation token the model never sees. Three modes (SAFE / PROVISION / FULL) with auto-expiry, a production write-gate that demands backup confirmation for irrecoverable ops, shell-quoted arguments, prompt-injection-tagged output, and a full JSON-lines audit log. 32 tools, TypeScript, MIT.

The Proxmox MCP you can hand the keys — VE + Backup Server + Mail Gateway on one audited trust core (plan, prove, undo, diagnose). 325 tools, MCP + A2A.

Outlit gives agents real-time customer understanding to automate customer operations through a hosted remote MCP server.

MCP server providing access to GPTHuman's API, the leading platform for rewriting AI-generated text into more natural, human-sounding prose, that successfully bypasses AI detectors.

The project tracker for teams of humans and AI agents — in plain files. MCP server + CLI, zero dependencies.

Local-first MCP server exposing 163 Textavia developer utilities for JSON formatting and validation, Base64 encode/decode, CSV conversion, Markdown tables, regex testing, hashing, text cleanup, case conversion, random values, timestamps, privacy scrubbing, and developer conversions. https://textavia.com

An AST-based Model Context Protocol (MCP) server that provides token-efficient codebase skeletons to LLM agents (like Cursor, Claude Desktop, and Antigravity). Instead of sending full raw source code files to the LLM, this server exposes structural "skeletons" (imports, exports, signatures, and JSDoc comments) of files and directories. This reduces token context sizes by 80% to 95% during codebase exploration and navigation.

**Picsart MCP — 150+ AI Models for Images, Video & Audio, One Connection** Picsart's AI Playground brings together more than 150 generative AI models from 24+ providers into one place. The MCP server is the developer and agent interface to that playground — a single connection that gives any AI agent or coding assistant the ability to generate images, video, audio, and text without managing a tangle of separate API keys, billing accounts, or SDKs. **What it generates** - **Images** — Text-to-image, style transfer, AI upscaling, background removal, image enhancement. Powered by models including Flux and other leading image generation providers. - **Video** — Text-to-video and image-to-video generation with top-tier models: Sora, Kling, and Veo, with more added regularly. - **Audio** — Voice synthesis, sound generation, and audio creation via ElevenLabs and other audio providers. - **Text** — Creative copywriting, captions, and content generation integrated directly into multi-modal workflows. **Why it's different from calling model APIs directly** Most creative AI workflows end up routing through three or four separate APIs — one for image generation, another for video, another for voice. Each comes with its own pricing model, rate limits, and integration overhead. Picsart MCP collapses that into one connection. Pricing is pay-per-generation. No monthly model subscriptions stacked on top of each other — access to the full model library for what you actually use. Compared to maintaining individual subscriptions across the leading generative AI providers, that difference runs to several thousand dollars a year. **Works wherever agents work** Picsart MCP follows the Model Context Protocol standard, which means it plugs into any MCP-compatible environment: - **Claude Desktop** — Add to `claude_desktop_config.json`, restart, and your Claude sessions can generate media on demand. - **Claude Code** — Run `claude mcp add` or drop the config into `.mcp.json` in your project root. Works immediately in the CLI and in IDE-integrated sessions. - **Cursor** — Add to `.cursor/mcp.json`. Every Cursor agent and AI panel gets creative generation capabilities. - **ChatGPT** — Compatible via MCP-to-plugin bridge. - **Hermes, Cowork, and other agentic frameworks** — Any tool that supports MCP transport (Streamable HTTP or stdio) connects without modification. **Get started in under a minute** ```json { "mcpServers": { "picsart-gen-ai": { "type": "http", "url": "https://api.picsart.com/gen-ai/mcp" } } } ``` Add this block to your MCP client's config file, drop in your Picsart API key, and restart. The full model library is available immediately — no additional setup, no per-model configuration. **Built for agentic workflows** The tools exposed by Picsart MCP are designed to work as steps inside larger agent pipelines, not just one-off generation requests. A workflow might look like: agent receives a brief → generates a hero image with Flux → generates a short video loop with Kling → synthesizes a voiceover with ElevenLabs → returns all three assets in one pass. That entire sequence runs through a single MCP server, from a single API key, billed per generation. **Part of the Picsart AI Playground** The MCP server is one interface to Picsart's broader AI Playground — a platform that aggregates the world's leading generative AI models so creators, developers, and agents can access them without vendor lock-in. The Playground is also available via direct API and web UI for non-agent workflows.

AI-native CAD inspection, no CAD license required. CAD MCP Server bundles a WebAssembly Open CASCADE kernel so LLM tools can read, measure, and compare STEP files locally. The server returns measured facts; your AI assistant interprets engineering meaning.

Your AI shipped it. Mushi tells you why it broke — plain diagnosis and a paste-ready fix prompt in Cursor. No second LLM key.

Open-source MCP server and PreToolUse hook for Claude Code. Exposes 15 MCP tools that let Claude inspect its own context-hygiene stats mid-session (token waste, duplicate reads, drift, cost forecasts), and caps verbose tool output plus dedups same-session file re-reads to keep context clean. Zero network calls in normal use; all local.

Read-only MCP server giving AI agents access to your live MyLinedChart desktop workspace. Exposes six read-only tools: get_chart_context (symbol, timeframe, provider, connection state, feed type, counts), get_candles (OHLCV bars, up to 2,000), get_drawings (trend lines, levels, labels), get_indicators (name, parameters, placement, visibility), get_provider_status (IBKR connector state, feed type), and get_trades (the session's executed trades/fills as chart markers; populated for IBKR users via session fills). Everything stays on your machine — no data leaves the process, secrets are redacted, and there is no trading, order entry, or mutation of any kind. Requires the MyLinedChart desktop app. Install: npm install -g @mylinedchart/mcp-chart-context. Docs: https://mylinedchart.com/mcp

Save your AI chat's working state — the decisions, context and plan — and load it in any other model, client or provider. Verified on save, secrets stripped.

AI Agent Context Versioning and Synchronization

MCP server for Zdravo AI — personal and org memory for AI agents. Save and search memories from Claude, ChatGPT, Gemini, and other AI platforms.

Omnicall — every tool an AI agent needs in one OpenAI-compatible endpoint: 247 LLMs (GPT, Claude, Gemini, Grok, DeepSeek, Llama, Qwen, Mistral), image / video / voice / music generation, live crypto / DEX / DeFi / prediction-market data, web search, X/Twitter profiles, and on-chain wallet reads. Pay per call in USDC on Base or Solana (or a funded credit key) — no API key, no signup, priced to beat the rest.

Daily, Ed25519-signed security intelligence for AI-agent stacks: CVEs, supply-chain incidents and advisories (GHSA/CISA-KEV/OSV/NVD + national-CERT) assessed for MCP/LLM-proxy impact, cross-validated and signed. Paid via x402 ($0.10 USDC on Base); free sample, list_available, check_affected, get_since.

Build, deploy, and manage production-ready APIs and workflow automations directly from your AI assistant. Cloudgate Builder lets Claude, ChatGPT, Cursor, and other MCP clients create workflows, publish REST APIs, manage integrations, and deploy instantly using secure OAuth authentication.

Build, deploy, and manage production-ready APIs and workflow automations directly from your AI assistant. Cloudgate Builder lets Claude, ChatGPT, Cursor, and other MCP clients create workflows, publish REST APIs, manage integrations, and deploy instantly using secure OAuth authentication.

LoreDocs organizes the reference knowledge your AI agent needs to do real work: architecture documents, API contracts, domain rules, configuration references. Everything lives in per-project vaults, versioned, searchable, and injectable into context with a single MCP call. Compatible with Claude, Cursor, Codex, and Hermes Agent. 36 MCP tools for comprehensive AI access. Local-first SQLite storage — no cloud, no external dependencies. Free tier includes 3 vaults. Pro is $9/month for unlimited.

Every AI session starts from zero. LoreConvo fixes that by saving your conversation context, decisions, and project state to a local SQLite database — and automatically loading the most relevant sessions at the start of each new conversation. Compatible with Claude, Cursor, Codex, and Hermes Agent. 14 MCP tools for programmatic access. 6 CLI commands for human debugging. Local-first: your data never leaves your machine. Free tier includes 50 sessions. Pro is $8/month for unlimited.

Structured, part-aware 3D generation for AI agents. Generates named-part GLB assets with intact hierarchy, real joints, and browser-based editing sessions.

Local, on-device text-to-speech for Claude & Cursor. No API key, no cloud. Powered by Supertonic 3 — 10 voices, 31 languages, inline expression tags (<laugh>, <pause>, etc.). ~820ms synthesis on Apple Silicon. MIT licensed.

Durable, local-first memory for AI coding agents over MCP — zero-dependency, curated & semantically de-duped, you own the data (SQLite + Markdown). Works with Claude Code, Codex & any MCP host.

Agent-native search engine with live web research optimized for AI agents.

Rock.so MCP connects your Rock workspace to any MCP client. With a Personal Access Token, your AI assistant works across all your spaces as you: universal search, plus read and write for messages, tasks, notes, and files. Hosted over Streamable HTTP with Bearer auth, on every Rock plan including Free.

On-brand website imagery, generated straight into your codebase. Plug this MCP into your coding tool (Claude Code, Cursor…) and every page you build ships with ultra-realistic, style-locked images: one consistent art direction across the whole site, recurring characters with the same face in every scene, believable UGC-style avatars for review sections, plus logo, favicon pack and og:image, all delivered as optimised WebP with proper ALT text, patched directly into your code. The heavy lifting (art direction, generation, billing) runs on the hosted Distribea engine.

Safe-by-default local stdio MCP server for the official Toss Securities Open API. Read-only market/account tools are available by default; order operations require explicit trading gates, dry-run defaults, confirmation, and max-order safeguards.

GPT Image 2 transforms text prompts into stunning 4K images, featuring highly accurate, readable text and photorealistic scenes. It offers advanced capabilities like multilingual rendering, custom dimensions, and image-to-image editing, making it ideal for product photography, ad creatives, concept art, and UI mockups.

xpenser.cleverbrush.com is a ln open-source, self-hostable personal expense and income tracking app supporting different currencies, categories, vendors, automated invoice parsing, AI geberated reports and more.

Generate, edit, and upscale AI video & images from any agent — VicSee as a set of MCP tools. Multi-model (Seedance, Veo, Kling, FLUX, Nano Banana), text or image to video or image, with async task polling. Six tools: list models, generate, poll task, upscale image, upscale video, check credits.

The best free Gantt chart maker — no signup, works offline, real-time collaboration. Its built-in MCP server lets Claude (web, Desktop, or Code) list, create, and edit your project timelines by chatting, using your own OAuth account.

EGC is a local runtime that gives every AI coding tool you use persistent memory. At the end of each session, the AI saves what it learned about your project: the decisions you made, what failed, your preferences, what comes next. At the start of the next session, it loads that state back. One install covers Claude Code, Cursor, Gemini CLI, Windsurf, and more. This is what EGC looks like in action:- You open Claude Code on a project you haven't touched in two weeks. Without typing anything: State loaded from egc-memory via ~/.egc/state/Projects--MyApp.md Context and preferences acknowledged (terse responses). Ready to pick up the next items: • Test full install on a clean machine • Add GEMINI.md with session memory protocol • Publish v1.0.1 fix to npm after clean install test passes • Add mcp_server_count to audit.js EGC lets you have extremely long context that is shared across sessions and tools, which is required when building production-level applications. Stop using /compact wherever your context reaches 80% and prevent the rot; adopt EGC.

AgentBrush (https://agentbrush.dev) is an MCP server that lets AI coding agents automate image generation inside their workflows: the agent calls and chains the tools (generate, remove-background, save-as-reference) without leaving the editor. Feed it your brand once (colors, fonts, reference images) and every product shot, app icon, OG card, game sprite or mascot lands on-brand and drops straight into your repo. Includes style presets, reference-image consistency, a mask editor for region edits, legible multilingual text, and free local background removal. For Claude Code, Cursor, Windsurf and any MCP client..

Open-source MCP server that gives Claude persistent memory. Your AI keeps your standards, decisions, and reasons across sessions. Recalled by meaning, not keywords. Runs fully local: on-device semantic search, no cloud, no API keys. macOS, Windows, Linux. MIT.

Multi-agent shared brain across Claude Code/Desktop, Codex, Antigravity CLI (agy), Gemini, Copilot, and VS Code. One local SQLite store for threads, notes, verbatim quotes, and a dialectic user model; spawn/broadcast/whisper/inbox multi-agent coordination primitives; autonomous background loops that materialize SKILL.md skills into every connected CLI's skills root. Install: pipx install 'threadkeeper[semantic]' && thread-keeper-setup

GPT Image 2 AI Image Editor MCP is a remote MCP server that lets AI agents and MCP-compatible clients generate and edit images through GPT Image 2. It supports text-to-image, image-to-image, model listing, and generation status queries via a Streamable HTTP JSON-RPC endpoint. Users can connect with an API key and call image generation tools directly from Claude, Cursor, ChatGPT, or other agent workflows.

An MCP server that flattens Claude Code sessions — keeping every prompt and event verbatim while reclaiming context tokens, so you resume the exact same raw conversation at a lower token count instead of compacting it into a lossy summary. It moves bulky tool output (large file reads, command logs, base64 screenshots) into a sidecar file, leaving a tiny retrievable reference in its place. Crash-safe, idempotent, and fully reversible. Real example from the README: a 317,236-token session flattened to 182,287 tokens.

An MCP (Model Context Protocol) server that generates advanced mock 2D PNG assets for games prototypes — directly from any MCP-compatible AI client such as Claude Desktop. This MCP is engine-agnostic and works with any game engine that supports PNG import: Godot Unity Unreal Engine GameMaker Construct RPG Maker And many more... Create placeholder sprites, UI elements, health bars, spritesheets, and more with full support for gradients, patterns, transparency, text rotation, and auto-scaling — all without opening an image editor. Each generated PNG embeds rich JSON metadata (dimensions, color, shape, description) directly in its EXIF data, so AI models without vision can still understand what an asset contains.

Documentation and assembly review for @nhtio/adk.

Persistent memory and shared context for AI coding tools. Two local MCP servers give every AI coding tool a shared, permanent memory of your project across sessions and tools.

Human-in-the-loop approvals & notifications for AI agents via WhatsApp (MCP server)

Runtime Cognitive Cortex for AI coding workflows. Contorium provides workspace awareness, project memory, runtime monitoring, MCP context synchronization, and optional cognitive insights without taking control away from developers.

X-ray your software architecture from any AI assistant: manage ArchiMate blueprints, score them against SOC 2, ISO 27001, GDPR, HIPAA and PCI-DSS, and pull generated code artifacts — all over MCP.

Official Black Forest Labs MCP server for FLUX image generation and editing — text-to-image, image-to-image, and multi-reference editing via the hosted endpoint at mcp.bfl.ai.

SCOUTS-AI MCP server: drop-in web_search tool backed by the no-key SCOUTS-AI API (https://scouts-ai.com). For AI agents, RAG pipelines, answer engines, GEO workflows.

Official Model Context Protocol (MCP) server to search, filter, and retrieve live remote job listings from the Jobicy database in real-time. Works with Claude Code, Cursor, Cline, and custom AI agents.

Chronicle MCP is a high-performance, universal local chat history archive connector designed specifically for Model Context Protocol (MCP) clients like Cursor, Claude Code, and daily development environments. Built natively in Python, it serves as an intelligent local storage and indexing layer that bridges your historical workspace transcripts directly into your active LLM context window. The tool addresses a critical bottleneck in AI-assisted engineering: context window bloat. By locally cleaning, formatting, caching, and dynamically chunking imported historical data, Chronicle MCP reduces context token usage by up to 40% while keeping your assistant fully informed of past decisions, codebase iterations, and project parameters.

Cruncher MCP: The Scientific Calculator MCP Server A powerful scientific calculator for your AI assistant, built as a Model Context Protocol (MCP) server. Cruncher allows compatible AI clients (like Claude Desktop) to perform complex mathematical calculations, handle memory, perform statistical analysis, and access scientific constants with a simple, secure, and standardized interface.

MCP-native AI SRE. Aient exposes your production OpenTelemetry problems, traces, and logs over the Model Context Protocol, plus an AI remediation loop that opens a reviewed GitHub fix PR and verifies in production (reopening on regression). Tools: list_problems, get_problem, query_traces, detect_anomalies, request_problem_remediation. Remote streamable-HTTP at https://aient.ai/mcp; auth via OAuth (scopes aient.mcp.read / aient.mcp.write). Human-in-the-loop by default. Request access at aient.ai.

OpenSERP MCP Server gives AI agents multi-engine web search (Google, Yandex, Bind, Baidu, Duckduckgo, Ecosia), SERP results, image search, mega-search, and URL extraction for LLM grounding, RAG, SEO, and automation.

Persistent memory for AI agents. Give your coding assistant organizational context that compounds — search memories with hybrid retrieval, store anything with auto entity extraction, and query a living knowledge graph that gets smarter over time.

Tribal captures the engineering knowledge that does not get written down in code or tickets. The reasoning behind a load-bearing decision, the heuristic someone keeps reaching for, the breakthrough that closed a gnarly bug. It runs as an MCP server, ingests text on demand, and exposes a graph of items connected by what they support, contradict, or refine. Your agent harness talks to it the same way it talks to any other MCP-compatible tool.

clockify-mcp exposes the Clockify time-tracking API as Model Context Protocol tools so any MCP client (Claude Desktop, Cursor, etc.) can query and manage time-tracking data in natural language. Covers 18 domains: workspaces, users, groups, clients, projects, tasks, tags, time entries, reports (detailed/summary/weekly/attendance/expense + PDF/CSV/XLSX export), shared reports, time off, holidays, expenses, approvals, custom fields, scheduling, invoices, and webhooks. 48 read tools are always available; 64 write tools are opt-in via CLOCKIFY_ACCESS_MODE (read | time-tracking | full). Auth is a single Clockify API key (X-Api-Key). Optional OpenTelemetry OTLP export for traces/metrics/logs.

Huckleberry MCP server for Claude, Cursor, and other AI assistants. Query and log baby sleep, feeds, diapers, growth, pumping, and solids.

MCP server for freshjots.com

GraphPilot gives coding agents persistent structural knowledge of a codebase. It parses TypeScript/JavaScript with tree-sitter into a graph of symbols and call edges, then exposes that graph over MCP so the agent can recall definitions, find callers, and trace change impact without re-reading files every conversation. Local-first: no network, no telemetry, no code ever leaves your machine. Four tools — gp_index, gp_recall, gp_callers, gp_impact.

Static technical-debt analysis across 14 languages, exposed as MCP tools and resources

Turn any published podcast into clean Markdown with real speaker names — not "Speaker 1." Three tools: search_podcasts (by text query or a pasted Spotify/YouTube URL), get_transcript (named, timestamped Markdown ready for LLMs and RAG), and get_balance. It's a transcript-retrieval API, not speech-to-text: it works on already-published shows, so you skip uploading audio and diarization — typically 5–10× cheaper. Try it free with the demo key pt_demo (no signup).

Discover verified deep-tech engineering bounties from any AI agent. Every bounty is funded in Stripe escrow before listing, and every submission is AI-verified before the buyer sees it. One tool: search_bounties.

GPT Scrambler is a free online tool that helps you rewrite AI-generated text and make it sound natural, human, and original. Whether it's a ChatGPT essay, blog post, or email — our AI writing scrambler helps you bypass AI detectors and create authentic content in seconds.

MCP server for AI image generation, video generation, music creation, text-to-speech, and LLM chat. 130+ models from Flux, Kling, Seedance, Veo, Suno, ElevenLabs, Claude, GPT, Gemini, and more. Browse models, check pricing, create tasks, and poll results. Free model discovery without API key. Works with Claude Code, Cursor, Codex, Windsurf, and any MCP host.

Exposes 280+ free dev/security tools from onlinecybertools.com as MCP tools in one server: Base64/URL/JWT encoders, MD5/SHA/HMAC/bcrypt/argon2 hashes, JSON/YAML/XML formatters, regex tester, network diagnostics (ping/traceroute/dig/whois/SSL/SPF/DMARC), and OSINT lookups. No auth, no API key.

Forge is Voxell's hosted text-embedding API. This MCP server exposes two tools — embed (turn text into vectors) and list_models — so any MCP-compatible agent can generate embeddings for semantic search, RAG, clustering, and dedup. Three tiers let you dial quality against cost: turbo (1024d, fast and cheap), pro (2560d), and ultra (4096d — Qwen3-Embedding-8B, ~75+ average task score on MTEB and currently #4 on MTEB English; the models above it are research-only, so it's the top usable model). Matryoshka dimensions let you truncate to fewer dims — re-normalized — for cheaper vectors. Requires a FORGE_API_KEY (free tier at dash.voxell.ai). Embeddings only — no data is stored.

MCP server for intelligent code context — 87%+ token savings, zero resource overhead. An MCP context engine built on AST call graphs + BM25 ranking, delivering precise code context retrieval for AI coding assistants. No GPU, no external services, pure Python, ready to use out of the box.

Read-only MCP server for searching, comparing, and recommending 18,000+ UK gyms using LocalGym data.

Job platform for AI agents. Connect Claude, Cursor, or any MCP client. Search tech companies by their real stack, star the ones you'd actually work for, save jobs to your shortlist. Through conversation, populate your profile from a CV, manage applications, and let your agent surface roles that fit your skills. Tech-stack data comes from real job postings, not declared tags.

Give your AI agent real vehicle data. An MCP server that lets Claude, Cursor, ChatGPT, or any MCP-capable client query the CarVector API natively — vehicle specs, representative images, federal recalls, and OBD-II diagnostic trouble codes. Models hallucinate car data. They invent horsepower numbers, miss recalls filed last week, and guess at what a trouble code means. carvector-mcp gives your agent structured, sourced answers it can cite instead of a confident guess.

Agentpack is a local-first continuity layer for AI coding agents. It stores reviewed task state inside a repo-local .agentpack/ ledger so Codex, Claude Code, Cursor, Claude Desktop, and other MCP clients can resume work without rebuilding context from chat history. Agents can record durable decisions, failed approaches, evidence, reviewed source conclusions, task passports, and checkpoints, then load compact resume context under a token budget. Git keeps the code history; Agentpack keeps the task state a future agent needs to continue safely. Agentpack runs locally, exposes a stdio MCP server, has zero runtime dependencies, makes no network calls during normal operation, and is designed for repo-scoped coding workflows rather than general chat memory.

mcp-cpp-project-indexer is a deterministic C++ source-range indexer for large, module-heavy projects and MCP-based AI code navigation. It is not a compiler, LSP replacement, refactoring engine, semantic analyzer, or call-graph builder. Its job is simple: Find code. Read code. Do not guess code. The indexer maps C++ symbols, files, and C++20 modules to exact source ranges so an AI can read only the code it needs.

The TouchDesigner MCP server — describe a visual to Claude, Cursor, or Codex, and it builds a real, playable node network (audio-reactive, generative, particle, 3D, feedback) with live knobs + MIDI/OSC/DMX, then checks for errors and previews its own work.

HashA2A — Agent-to-Agent Prediction Market Intelligence Decentralized data marketplace on Hedera mainnet. AI agents buy cross-validated prediction market data from 5 providers (Polymarket, Kalshi, PredictIt, Manifold, Hyperliquid) via HBAR or USDC micropayments. 19 MCP tools: get_price, list_assets, get_market_data, analyze_market, deep_research, scan_arbitrage, list_providers, aggregate_market_data, verified_feed, get_agent_profile, and 8 more. Features: Multi-oracle price feeds (36 assets × 5 sources), real-time arbitrage detection, AI-powered market analysis (LangChain + GPT-5-nano), reverse auctions for best price/quality, and enterprise Hedera Agent Kit tools (account, topic, transfer). Payment: HIP-991 HBAR auto-fees, x402 USDC on Base, or Circle Agent Wallet (gasless).

HashA2A — Agent-to-Agent Prediction Market Intelligence Decentralized data marketplace on Hedera mainnet. AI agents buy cross-validated prediction market data from 5 providers (Polymarket, Kalshi, PredictIt, Manifold, Hyperliquid) via HBAR or USDC micropayments. 19 MCP tools: get_price, list_assets, get_market_data, analyze_market, deep_research, scan_arbitrage, list_providers, aggregate_market_data, verified_feed, get_agent_profile, and 8 more. Features: Multi-oracle price feeds (36 assets × 5 sources), real-time arbitrage detection, AI-powered market analysis (LangChain + GPT-5-nano), reverse auctions for best price/quality, and enterprise Hedera Agent Kit tools (account, topic, transfer). Payment: HIP-991 HBAR auto-fees, x402 USDC on Base, or Circle Agent Wallet (gasless).

One shared AI setup for your team. Stop losing prompts in chats and docs. Save one shared setup, tailor it for your team, and use it across ChatGPT, Codex, Claude, Figma, and more.

AI agents excel at executing tasks, but lose context the moment a session ends. Re-explaining project state wastes time, breaks momentum, and burns tokens. **CWIM Kanban bridges the gap.** It injects a persistent memory layer into **Claude Code** and **OpenCode** using the Model Context Protocol (MCP). * 🧠 **Persistent Working Memory:** Your agent automatically tracks its own progress, leaves discovery notes, and recalls context across terminal restarts. * 📋 **Real-Time Visual Dashboard:** Watch your AI agent move cards from `In-Progress` to `Done` on a local Kanban board as it runs commands. * 📂 **Git-Driven Session Isolation:** Auto-detects your repository. When you `cd` into a project, your AI's memory switches context instantly.

Read-only MCP server and JSON API for international country calling codes, ISO country codes, E.164 dial codes, regions, trunk prefixes, and NANP area codes.

Generate AI images, videos, music, sound effects, and speech directly inside any AI assistant. Results appear inline in chat — no tab switching. Supports Claude, Cursor, Windsurf, and any MCP client.

Skills for AI agents that need public data. Run KOL pricing, creator research, social listening, and competitor monitoring directly inside Codex or Claude Code. UnifAPI MCP supplies the live public data; the agent does the work.

Share context, detect file conflicts, and send messages across parallel Claude Code sessions

MCP server for Scriptivox AI transcription. Transcribe audio and video from URLs or local files in 119 languages, with speaker diarization, word-level timestamps, and SRT/VTT/text export. Bring transcription to Claude Desktop, Cursor, Cline, and any MCP-compatible client.

Local stdio MCP server for VRChat friends, worlds, groups, events, notifications, status, avatars, and VRCX history.

Humanize AI-generated text, run AI detection, and batch process content inside Claude. Built for SEO and content teams who need publish-ready copy without keyword drift. Three tools: Humanize (rewrites AI text while preserving exact keywords, entities, brand names, URLs, and document structure), Detect (scores text for AI patterns with actionable fix suggestions), and Batch Humanize (process up to 25 items per call with per-item keyword protection). No filesystem access, no browsing, no agent chains. Text in, text out.

HemmaBo is the host-owned trust layer for vacation rentals: every host runs 0%-commission direct bookings on their own domain, with host-signed, agent-verifiable offers that AI agents can discover, verify, and book directly. HemmaBo + VRP, 15 tools: an agent discovers a host's own domain, verifies a host-domain signed (VRP · Ed25519/JWKS) stay offer with live availability and exact price, and routes to the direct booking URL on the host's own domain.

MCP server for monitoring, managing and querying Informatica IDMC CDI jobs — start/stop jobs, check status, query activity logs via Claude, Cursor, and Agentforce

README.md--- license: apache-2.0 language: - en tags: - mcp-server - ai-gateway - security - rust - agent-framework - tool-enforcement - lmdb - rag - transformer - mesh-network - voice - android - termux - self-hosted - ai-safety - memory-system - flint - build-anchor - complexity-formula - agent-memory - p2p - quic - heed - self-learning - harness - ai-memory - persistent-memory - online-learning - agent-tools - tool-gateway - web-automation - browser-automation - social-media - p2p-communication - voice-synthesis - tts - embedded-database - zero-copy - code-search - filesystem - git - database pipeline_tag: text-generation --- ``` _____ _____ ______ _____ __ __ _____ _______ _____ _______ ______ / ____| __ \| ____| / ____| \/ | /\ | __ \__ __| / ____| /\|__ __| ____| | (___ | |__) | |__ | (___ | \ / | / \ | |__) | | | | | __ / \ | | | |__ \___ \| ___/| __| \___ \| |\/| | / /\ \ | _ / | | | | |_ | / /\ \ | | | __| ____) | | | | ____) | | | |/ ____ \| | \ \ | | | |__| |/ ____ \| | | |____ |_____/|_| |_| |_____/|_| |_/_/ \_\_| \_\ |_| \_____/_/ \_\_| |______| ``` # SPF Smart Gateway v3.0.0 **MCP Server Gateway with Multi-Layer Security Enforcement, Agent Memory, FLINT Transformer, Mesh Network, and 81 Gated Tools** > **NOTE: Full system upload still in progress.** Not all files are present yet. Repository is actively being populated — some modules may be missing until upload completes. Copyright (C) 2026 Joseph Stone — All Rights Reserved --- ## Quick Start ```bash # Clone into home folder git clone <repo-url> ~/SPFsmartGATE # Or for clones/SWARMagents: # ~/SWARMagents/1/SPFsmartGATE cd SPFsmartGATE cargo build --release # Copy optimized binary cp ~/SPFsmartGATE/target/release/spf-smart-gate ~/SPFsmartGATE/LIVE/BIN/spf-smart-gate # Configure MCP server filepath nano ~/SPFsmartGATE/LIVE/LMDB5/.mcp.json # Install Claude CLI in project directory # Use included configs, deny native Claude CLI tools # ~/SPFsmartGATE/LIVE/LMDB5/.claude.json # ~/SPFsmartGATE/LIVE/LMDB5/.claude/settings.json # Boot into flat-file agent runtime cd ~/SPFsmartGATE/LIVE/LMDB5 && claude # Boot into LMDB-backed agent runtime cd ~/SPFsmartGATE/LIVE/LMDB5.DB && claude ``` ### Route Other Models Through Claude CLI Adjust `~/SPFsmartGATE/LIVE/LMDB5/.claude/settings.local.json` with your model choice and API key. Uses OpenRouter for API and agent selection. Swap agents without changing sessions or losing project data. ### Build Notes - Cross-compiles on **Android** and **Linux** with minimal installation - Only rebuild on first boot or after system modifications - Binary: `~/SPFsmartGATE/LIVE/BIN/spf-smart-gate/spf-smart-gate` --- ## Overview SPF Smart Gateway is a **Rust-based MCP (Model Context Protocol) server** that acts as a security gateway for AI tool calls. Every file operation, bash command, brain query, and mesh call routes through compiled Rust enforcement logic. **No AI hallucination gets past the gate.** ### Web Agent Feature SPF agents can directly interact with the web and social media platforms through `spf_web_api` — a full HTTP client supporting GET, POST, PUT, DELETE, PATCH with custom headers and JSON body. Tested and working. **What agents can do:** - Post to X/Twitter, Facebook, Instagram, Reddit via their APIs - Reply to comments, send messages, manage accounts - Make authenticated API calls to any platform with stored API keys - Search, fetch, and download web content All web API calls pass through the 6-step gate pipeline with rate limiting (30-120 calls/min), content inspection, and full audit logging. Agents never touch the open web unmonitored. ### Why Heed + LMDB All persistent storage — config, agent state, brain vectors, session logs, gate training data — runs through **[heed](https://github.com/meilisearch/heed)**, a safe Rust wrapper over LMDB. This is what makes SPF extremely fast with a low memory footprint: - **Zero-copy reads** — heed maps LMDB pages directly into memory, no serialization overhead - **No server process** — LMDB is a memory-mapped B-tree library, not a database daemon - **ACID transactions** — single-writer, multi-reader with no lock contention on reads - **Sub-millisecond lookups** — B-tree index, not hash scanning - **Tiny footprint** — entire 138K+ memory store runs in-process with minimal RAM - **Phone-friendly** — designed for Android from day one; heed compiles cleanly on ARM64 Every tool call, brain search, and memory promotion goes through heed → LMDB. No network hops, no subprocess calls, no SQL parsing. The gate, brain, agent state, and FLINT training all share the same embedded database engine. Two agent runtimes: - **Flat files** — `LIVE/LMDB5/` (session state in markdown) - **LMDB database** — `LIVE/LMDB5.DB/` (session state in LMDB for persistence) Twin folder architecture: flat-file data uploaded via SPF CLI fs tools (user-only access). All agent tool calls are gated, validated, and audited. --- ## Architecture ``` ┌─────────────────────────────────────────────────────────────────┐ │ SPF Smart Gateway v3.0.0 │ │ 42 Rust modules │ ├─────────────────────────────────────────────────────────────────┤ │ MCP Server (JSON-RPC 2.0 over stdio) │ │ 81 tools │ tool alias map │ Qwen/LLM compatibility │ ├─────────────────────────────────────────────────────────────────┤ │ GATE (6-Step Pipeline) │ │ Step 0: Source logging │ │ Step 1: Rate limiting │ │ Step 2: Complexity calculation (SPF formula) │ │ Step 3: Validation (per-tool: paths, commands, Build Anchor) │ │ Step 4: Content inspection (credentials, injection) │ │ Step 5: Max mode escalation │ ├──────────┬──────────┬──────────┬──────────┬─────────────────────┤ │ FLINT │ Brain │ Mesh │ Voice │ Browser/RAG │ │ (encoder-│ (vectors │ (P2P QUIC│ (TTS/STT │ (reverse proxy │ │ decoder │ LMDB + │ Ed25519 │ espeak- │ search, fetch, │ │ ~5M │ MiniLM) │ iroh) │ ng FFI) │ RSS, web tools) │ │ params) │ │ │ │ │ ├──────────┴──────────┴──────────┴──────────┴─────────────────────┤ │ LMDB Storage Layer (heed) │ │ SPF_CONFIG │ TMP_DB │ AGENT_STATE │ Brain │ Gate Training │ │ All zero-copy reads via heed safe Rust bindings │ └─────────────────────────────────────────────────────────────────┘ ``` ### Module Inventory (42 modules) `paths`, `calculate`, `config`, `gate`, `inspect`, `mcp`, `session`, `storage`, `validate`, `web`, `http`, `dispatch`, `identity`, `mesh`, `fs`, `config_db`, `tmp_db`, `agent_state`, `tensor`, `tokenizer`, `framing`, `attention`, `ffn`, `encoder`, `decoder`, `transformer`, `checkpoint`, `gate_training`, `transformer_tools`, `train`, `learning`, `pipeline`, `worker`, `network`, `chat`, `voice`, `utf8_safe`, `brain_local`, `flint_memory`, `browser`, `orchestrator`, `channel` --- ## The SPF Formula ### Complexity Calculation ``` C = (basic ^ 1) + (dependencies ^ 7) + (complex ^ 10) + (files × 10) ``` ### Dynamic Analysis Allocation ``` a_optimal(C) = W_eff × (1 - 1/ln(C + e)) ``` Where `W_eff = 40,000` tokens and `e = Euler's number` ### Tier Allocation | Tier | C Range | Analyze | Build | Verify Passes | Approval | |------|---------|---------|-------|---------------|----------| | SIMPLE | < 500 | 40% | 60% | 1 | No | | LIGHT | < 2,000 | 60% | 40% | 1 | No | | MEDIUM | < 10,000 | 75% | 25% | 2 | No | | CRITICAL | > 10,000 | 95% | 5% | 3 | **Required** | ### Master Equation (Subtask Success) ``` P(success) = 1 - PRODUCT(1 - P_i) for i=1..D subtasks P_i = Q(a) × L(m) × V(v) × B(b) Q(a) = 1 - e^(-0.00004 × a) — Quality from analysis depth L(m) = 1 - 0.20^(m/2000) — Lookup from external memory V(v) = 1 - (1 - 0.75)^v — Verification accuracy B(b) = checks_done / checks_required — Build Anchor compliance ``` --- ## Security ### Gate Enforcement (6 Steps) Every tool call passes through `gate::process()` — compiled Rust, no runtime bypass. | Step | What | How | |------|------|-----| | 0 | Source logging | Identifies caller (Stdio, Transformer, Mesh, HTTP) | | 1 | Rate limiting | Per-tool limits (30–120 calls/min) | | 2 | Complexity calc | SPF formula → C value, tier, allocation | | 3 | Validation | Per-tool validator (paths, commands, anchors) | | 4 | Content inspection | Credential patterns, shell injection, path traversal | | 5 | Max mode | Escalation to CRITICAL tier on warnings | ### Build Anchor Protocol Files must be **read before they can be edited or overwritten**. Prevents AI hallucinations from blindly modifying files without understanding contents. - `Read` tracks files in `session.files_read` - `Edit` and `Write` check against this list - `Bash` write-class commands check target file reads - Violations: blocked (Max mode) or warned (Soft mode) ### Content Inspection Scans written/stored content for: - **Credential patterns**: API keys (sk-), AWS keys (AKIA), GitHub tokens (ghp_), Slack tokens, private keys, hardcoded passwords - **Shell injection**: Command substitution `$()`, backticks, eval/exec - **Path traversal**: `../` sequences - **Blocked path references**: Content mentioning system paths ### Blocked Paths Default blocked: `/tmp`, `/etc`, `/usr`, `/system`, `/data/data/com.termux/files/usr` ### Command Whitelist (Stage 0) Bash commands checked against sandbox and user-filesystem whitelists. Each command segment validated independently. Destructive commands (rm, chmod 777) blocked even if whitelisted. ### Default Deny Unknown tools blocked until explicitly added to the gate allowlist. --- ## MCP Tools (81 Total) ### Core Gate Tools | Tool | Description | |------|-------------| | `spf_calculate` | Calculate complexity score without executing. Returns C value, tier, allocation | | `spf_status` | Gateway status: session metrics, enforcement mode, complexity budget | | `spf_session` | Full session state: files read/written, action history, anchor ratio | ### Gated File Operations | Tool | Description | |------|-------------| | `Read` | Gated file read. Tracks for Build Anchor Protocol. Binary-safe | | `Write` | Gated file write. Validates Build Anchor, blocked paths, file size | | `Edit` | Gated file edit. Validates Build Anchor, blocked paths, change size | | `Bash` | Gated bash execution. Validates dangerous commands, /tmp access, git force | | `Glob` | Fast file pattern matching. Supports `**/*.rs`, `src/**/*.ts` | | `Grep` | Search file contents using regex. Built on ripgrep | ### Brain / Memory Tools | Tool | Description | |------|-------------| | `spf_brain_search` | Semantic vector search across collections (MiniLM-L6-v2, 384d) | | `spf_brain_recall` | Full document retrieval by semantic query | | `spf_brain_context` | Bounded context retrieval for prompt injection | | `spf_brain_store` | Store document in brain (FLINT-internal, source-gated) | | `spf_flint_store` | Agent memory store — bypasses brain write gate. Brain vectors + Working tier | | `spf_brain_index` | Index a file or directory into a brain collection | | `spf_brain_list` | List all indexed collections with document counts | | `spf_brain_status` | Brain system status: model state, storage size, collections | | `spf_brain_list_docs` | List stored documents in a collection | | `spf_brain_get_doc` | Retrieve a specific document by ID | ### Agent State Tools | Tool | Description | |------|-------------| | `spf_agent_stats` | AGENT_STATE LMDB statistics: memory count, sessions, state keys, tags | | `spf_agent_memory_search` | Search agent memories by content | | `spf_agent_memory_by_tag` | Get agent memories by tag | | `spf_agent_session_info` | Most recent session info | | `spf_agent_context` | Context summary for session continuity | ### FLINT Transformer Tools | Tool | Description | |------|-------------| | `spf_transformer_status` | FLINT transformer status: loaded, params, checkpoint, role | | `spf_transformer_infer` | Run inference: prompt → response. Returns generated tokens | | `spf_transformer_chat` | Multi-turn conversation with FLINT | | `spf_transformer_train` | Trigger manual training batch from accumulated gate signals | | `spf_transformer_metrics` | Learning metrics: loss, accuracy, gate alignment, training step | | `spf_flint_train_evil` | Mark a tool call as evil/harmful. Negative training signal | | `spf_flint_train_good` | Mark a tool call as good/safe. Positive training signal | | `spf_flint_execute` | Execute any SPF tool through FLINT worker mode (delegation) | ### Web Browser Tools **API tools (tested):** | Tool | Description | |------|-------------| | `spf_web_search` | Search the web (Brave API or DuckDuckGo) | | `spf_web_fetch` | Fetch URL and return clean readable text | | `spf_web_api` | Make HTTP API requests (GET/POST/PUT/DELETE/PATCH). Supports custom headers and JSON body — agents can directly interact with social media APIs (X/Twitter, Facebook, Instagram, Reddit, etc.) using stored API keys | | `spf_web_download` | Download a file from URL and save to disk | **Browser automation tools (in development — proxy starts, WebSocket bridge needs browser connection):** | Tool | Description | Status | |------|-------------|--------| | `spf_web_connect` | Initialize reverse proxy browser engine | Tested — works | | `spf_web_navigate` | Navigate browser to a URL (SSRF-validated) | Tested — works | | `spf_web_click` | Click a page element by CSS selector | In development — WebSocket timeout | | `spf_web_fill` | Type text into a form field by CSS selector | In development — WebSocket timeout | | `spf_web_select` | Query page elements by CSS selector | In development — WebSocket timeout | | `spf_web_eval` | Execute JavaScript on the current page | In development — WebSocket timeout | | `spf_web_screenshot` | Capture a screenshot of the current page | In development | | `spf_web_design` | Extract design brief: colours, fonts, spacing, components | In development | | `spf_web_page` | Structured page overview: title, headings, links, forms | In development | ### RAG Collector Tools | Tool | Description | |------|-------------| | `spf_rag_collect_web` | Search web and collect documents. Optional topic filter | | `spf_rag_collect_file` | Process a local file into brain | | `spf_rag_collect_folder` | Process all files in a folder | | `spf_rag_collect_drop` | Process files in DROP_HERE folder | | `spf_rag_index_gathered` | Index all documents in GATHERED to brain | | `spf_rag_dedupe` | Deduplicate a brain collection | | `spf_rag_status` | Collector status and stats | | `spf_rag_list_gathered` | List documents in GATHERED folder | | `spf_rag_bandwidth_status` | Bandwidth usage stats and limits | | `spf_rag_fetch_url` | Fetch a single URL with bandwidth limiting | | `spf_rag_collect_rss` | Collect from RSS/Atom feeds | | `spf_rag_list_feeds` | List configured RSS feeds | | `spf_rag_pending_searches` | Get pending SearchSeeker vectors (gaps needing fetch) | | `spf_rag_fulfill_search` | Mark a SearchSeeker as fulfilled after RAG fetch | | `spf_rag_smart_search` | Smart search with completeness check — triggers SearchSeeker if <80% | | `spf_rag_auto_fetch_gaps` | Automatically fetch data for all pending SearchSeekers | ### Mesh Network Tools | Tool | Description | |------|-------------| | `spf_mesh_status` | Mesh network status: role, team, identity | | `spf_mesh_peers` | List known/trusted mesh peers | | `spf_mesh_call` | Call a peer agent's tool via P2P mesh (Ed25519 authenticated) | ### Voice Tools | Tool | Description | |------|-------------| | `spf_voice_mode` | Voice pipeline control: start/stop audio, TTS (espeak-ng), mic capture | | `spf_voice_call` | Peer-to-peer voice calls: start, accept, reject, end, status | | `spf_voice_team` | Group voice channels: create, join, leave, add peers | ### Chat Tools | Tool | Description | |------|-------------| | `spf_chat_send` | Send text message to mesh peer via QUIC | | `spf_chat_history` | Chat message history (all conversations or specific) | | `spf_chat_rooms` | List active chat conversations with participant info | ### Network Pool Tools | Tool | Description | |------|-------------| | `spf_pool_status` | Pool status: worker roles, idle/busy counts, active tasks | | `spf_pool_assign` | Assign task to idle worker (NetAdmin only) | | `spf_pool_release` | Release worker and record proof of work receipt | ### Configuration Tools | Tool | Description | |------|-------------| | `spf_config_paths` | List all path rules (allowed/blocked) from SPF_CONFIG | | `spf_config_stats` | SPF_CONFIG LMDB statistics | ### Project Management Tools | Tool | Description | |------|-------------| | `spf_tmp_list` | List all registered projects with trust levels | | `spf_tmp_stats` | TMP_DB statistics: project count, access logs, resources | | `spf_tmp_get` | Get project info by path | | `spf_tmp_active` | Get the currently active project | ### Communication Hub | Tool | Description | |------|-------------| | `spf_channel` | Universal agent channel: create, join, leave, send, listen, history, list, connect (WS), disconnect, status | ### Notebook Tools | Tool | Description | |------|-------------| | `spf_notebook_edit` | Edit a Jupyter notebook cell (replace, insert, delete) | ### User-Only Tools (AI agents blocked) These tools are **hard-blocked** from AI agents at the gate level. User/system access only via SPF CLI: `spf_fs_exists`, `spf_fs_stat`, `spf_fs_ls`, `spf_fs_read`, `spf_fs_write`, `spf_fs_mkdir`, `spf_fs_rm`, `spf_fs_rename` --- ## FLINT Transformer Built-in encoder-decoder transformer for gate-aligned learning. | Property | Value | |----------|-------| | Architecture | Encoder-decoder | | Dimensions | 256d | | Heads | 8 | | Layers | 6 | | Parameters | ~5M | | Embeddings | all-MiniLM-L6-v2 (384d, in-process) | | Online learning | ON | | EWC lambda | 0.4 | | Learning rate | 1e-4 | | Replay buffer | 10,000 slots | | Checkpoint interval | 1,000 steps | | Training signal | Gate decisions (evil/FP labels) | ### Learning Pipeline | Phase | When | What | |-------|------|------| | PRE | Startup | init_brain() + index_knowledge_docs() + index_spf_sources() | | DURING | 30s loop | GateTrainingCollector → FLINT scores → route_signals → brain_store() | | AFTER | 1hr loop | Expire → Working→Fact → Fact→Pinned → auto-train (16+ tlog or 1hr) | ### Memory Lifecycle (Tiered Promotion) ``` Agent stores → Working (24hr) → Fact (7-day) → Pinned (permanent) ↓ ↓ ↓ Expire old Top 20% promote Never auto-expire ``` --- ## Brain System In-process vector memory using stoneshell-brain (Candle + LMDB + MiniLM-L6-v2). | Property | Value | |----------|-------| | Model | all-MiniLM-L6-v2 | | Embedding dim | 384 | | Chunk size | 512 | | Chunk overlap | 64 | | Storage | LMDB (vectors) + LIVE/BRAIN/DOCS/ (data files) | ### Collections | Collection | Purpose | |------------|---------| | `default` | General knowledge, web research, project docs | | `spf_source` | All src/*.rs modules indexed at boot | | `flint_results` | Tool call results (>2000 chars, before compression) | | `flint_training` | Gate decision signals, evil/FP labels | | `flint_knowledge` | User-dropped knowledge files (.md/.txt/.rs/.json) | | `flint_episodic` | Past FLINT Q+A pairs, behavioral patterns | | `session_state` | Current session metadata | ### Memory Triad (Redundant Persistence) Three systems — if any ONE fails, the other TWO recover: 1. **Brain** (vectors) — Semantic search, chunked knowledge 2. **STATUS** (sequential) — Current state, phase, next step 3. **Work Blocks** (structural) — Tasks, dependencies, confidence, progress 4. **Twin Folders** (evidence) — Data served for low-confidence work blocks --- ## Mesh Network P2P agent communication over QUIC (iroh library) with Ed25519 identity. **In development and testing.** | Feature | Status | |---------|--------| | P2P QUIC transport | In development | | Ed25519 identity | In development | | Peer discovery | In development | | Tool call proxying | In development | | Voice over mesh | In development | | Chat over mesh | In development | | Multi-agent coordination | In development | --- ## Voice Pipeline **Not yet tested.** Components built, awaiting integration testing. | Component | Technology | |-----------|-----------| | TTS | espeak-ng FFI (in-process) | | Codec | Opus (libopus.a) | | Audio | cpal + oboe-ext | | STT | Pending (JNI via Stone Shell Terminal) | --- ## Result Compression (FL-2) Three tiers based on result size: | Tier | Size | Behavior | |------|------|----------| | FULL | < 500 chars | Pass through unchanged | | SUMMARY | 500–5,000 | First 8 lines + last 3 lines + stats | | DIGEST | > 5,000 | First 200 chars + last 100 chars + stats + recall hint | Originals always preserved in brain (>2000 chars threshold) before compression. File reads never truncated (preserves non-Claude LLM compatibility). --- ## Build ```bash cd SPFsmartGATE cargo build --release # Deploy binary cp target/release/spf-smart-gate LIVE/BIN/spf-smart-gate/spf-smart-gate ``` ### Dependencies - Rust (stable) - **[heed](https://github.com/meilisearch/heed)** — safe Rust LMDB bindings. All persistent storage (config, agent state, brain vectors, training data) runs through heed → LMDB. Zero-copy reads, no server process, sub-millisecond lookups. The core reason SPF runs fast on a phone. - stoneshell-brain (Candle + MiniLM-L6-v2) - espeak-ng (TTS) - libopus (audio codec) - iroh (QUIC mesh) --- ## Configuration ### MCP Server Config `~/SPFsmartGATE/LIVE/LMDB5/.mcp.json` — points Claude CLI to the binary. ### Claude CLI Config `~/SPFsmartGATE/LIVE/LMDB5/.claude.json` — blocks native Claude CLI tools (26 tools denied). `~/SPFsmartGATE/LIVE/LMDB5/.claude/settings.json` — deny list for native tools. `~/SPFsmartGATE/LIVE/LMDB5/.claude/settings.local.json` — model routing (OpenRouter). ### SPF Config Enforcement mode (`soft` or `max`), blocked paths, allowed paths, formula weights — all in LMDB SPF_CONFIG database. --- ## File Structure ``` SPFsmartGATE/ ├── Cargo.toml # Rust project manifest (42 modules) ├── LICENSE # Apache-2.0 ├── README.md # This file ├── src/ │ ├── main.rs # CLI entry point │ ├── lib.rs # Library exports (42 pub mod) │ ├── gate.rs # Primary enforcement (6-step pipeline) │ ├── calculate.rs # SPF complexity formula │ ├── validate.rs # Rules validation (stages 0-6) │ ├── inspect.rs # Content inspection (creds, injection) │ ├── mcp.rs # MCP server (JSON-RPC 2.0, 81 tools) │ ├── dispatch.rs # Unified dispatch (all transports) │ ├── session.rs # Session state management │ ├── storage.rs # LMDB persistence │ ├── config.rs # Configuration types │ ├── brain_local.rs # In-process brain singleton │ ├── flint_memory.rs # Memory router + tiered promotion │ ├── agent_state.rs # Agent memory (LMDB5) │ ├── transformer.rs # FLINT model (encoder-decoder) │ ├── transformer_tools.rs # FLINT tool handlers │ ├── gate_training.rs # Training signal collection │ ├── train.rs # AdamW optimizer │ ├── tokenizer.rs # Tokenizer │ ├── tensor.rs # Tensor operations │ ├── attention.rs # Multi-head attention │ ├── ffn.rs # Feed-forward network │ ├── encoder.rs # Encoder stack │ ├── decoder.rs # Decoder stack │ ├── framing.rs # Message framing │ ├── checkpoint.rs # Model checkpoint save/load │ ├── learning.rs # Learning rate + EWC │ ├── pipeline.rs # Batch pipeline + API sessions │ ├── worker.rs # Worker pool │ ├── network.rs # Network pool + NetAdmin │ ├── mesh.rs # P2P QUIC mesh (iroh) │ ├── identity.rs # Ed25519 identity │ ├── chat.rs # Chat engine │ ├── voice.rs # Voice pipeline (TTS/STT) │ ├── web.rs # Web client │ ├── http.rs # HTTP server + reverse proxy │ ├── browser.rs # Browser automation │ ├── channel.rs # Universal channel hub │ ├── orchestrator.rs # Multi-agent orchestrator │ ├── config_db.rs # SPF_CONFIG LMDB │ ├── tmp_db.rs # TMP_DB LMDB │ ├── fs.rs # Virtual filesystem (LMDB) │ ├── paths.rs # Path utilities │ └── utf8_safe.rs # UTF-8 safe truncation ├── LIVE/ │ ├── BIN/spf-smart-gate/ # Deployed binary │ ├── BRAIN/DOCS/ # Brain data files │ ├── MODELS/ # FLINT checkpoints │ ├── SESSION/ # Session logs │ ├── LMDB5/ # Flat-file agent runtime │ └── LMDB5.DB/ # LMDB-backed agent runtime └── PROJECTS/PROJECTS/ └── DEPLOY/ # Agent workspace ``` --- ## Current Status | Component | Status | |-----------|--------| | MCP Server | 81 gated tools | | Gate Security | 6-step pipeline, compiled Rust enforcement | | Build Anchor | Read-before-write enforced | | Content Inspection | Credential + injection scanning | | FLINT Transformer | ~5M params, online learning, gate-aligned | | Brain | 7 collections, MiniLM-L6-v2, in-process | | Memory Triad | Brain + STATUS + Work Blocks + Twin Folders | | Tiered Promotion | Working → Fact → Pinned lifecycle | | Mesh Network | P2P QUIC, Ed25519, iroh — **in development and testing** | | Voice | TTS built (espeak-ng) — **not yet tested**, STT pending | | Chat | P2P messaging over mesh — **in development** | | RAG | Web search, RSS, file/folder indexing | | Web Agent | **Working** — spf_web_api tested (GET/POST with auth headers). Agents can interact with social media APIs | | Browser | API tools working (web_api, search, fetch). Browser automation (navigate/click/fill/select/eval) in development — proxy starts but WebSocket bridge needs browser connection | | Network Pool | Worker pool with proof of work | --- ## Notes - **1 developer** — not all features complete - **Gateway security**: approaching 100% - **All core tools**: 100% working - **Cross-compiles** on Android and Linux with minimal installation - **Agent cloning and specialization** supported - **50+ day continuous session** tested on Android phone - **Open source** — entire source code refreshes into transformer RAG system every reboot - Install in home folder, ensure file paths are correct in `.mcp.json` and `settings.local.json` - **Not all files have been uploaded yet** — repository is still being populated. Some modules may not be present until upload completes. --- ## License Licensed under the **Apache License 2.0**. See [LICENSE](LICENSE) for full terms. You are free to use, modify, and distribute this software, including for commercial purposes, provided you include the original copyright and license notice. **Author**: Joseph Stone **Email**: joepcstone@gmail.com *SPF (StoneCell Processing Formula), Build Anchor Protocol, and FLINT are proprietary designs of Joseph Stone.*

Pre-install risk gate for npm packages. Stops AI coding agents from running malicious or typosquatted packages before lifecycle scripts run.

Pexels MCP server — search and fetch free stock photos, videos, and curated collections from Pexels inside Claude, Cursor, Cline, and other MCP clients.

Stop re-explaining yourself to agents. Give it the right context right when it is needed. LLMs are stateless — each conversation starts from scratch. ContextBook gives your AI tools a persistent, searchable knowledge library to draw the right context when they need it. No bloatware, no pre-loaded junk. Just the right information, at the right time.

MCP server for AI-agent handoffs. A2CR saves client-encrypted WorkBaton checkpoints and WorkStash notes so MCP-capable clients can resume work without passing full chat history.

Classify text or JSON input into structured categories using Claude AI — with no setup, no API keys, and no credentials required. Connect directly and start classifying immediately. Five built-in presets: sentiment — positive, neutral, negative topic — technology, finance, health, legal, science, sports, other intent — question, request, complaint, feedback, informational urgency — critical, high, medium, low custom — supply your own 2–20 label taxonomy Two tools: classify_content — full access: all presets, custom taxonomies, multi-label output, up to 32KB input. Billed at $0.005 USDC per call via x402 micropayment on Base mainnet. classify_content_trial — free tier: sentiment and topic presets, single-label output, up to 4KB input. No payment required. Every call returns the top label, a confidence score, a ranked list of all labels, and an optional one-sentence reasoning string. Supports plain text and JSON input. Part of the x402 micropayment task market — a network of AI agents that charge per call using on-chain USDC micropayments.

Text Summarizer condenses long-form content into structured, actionable summaries so AI agents and applications can work with information faster and at lower cost. Feed it meeting notes, research articles, documentation, JSON payloads, or Markdown files and get back a clean summary in the format you need — a full prose paragraph, a bullet list of key points, a single headline, or a TL;DR — at the length you specify. Built for agents that process high volumes of text where passing the full content downstream is slow, expensive, or unnecessary. The compression ratio returned with every response lets callers track exactly how much content was reduced. No API keys or credentials required. Pay $0.005 USDC per call via x402 on Base mainnet. A free trial tool is included for integration testing — prose format, up to 4KB, no payment needed.