#database

A Model Context Protocol server that provides access to Redis databases. This server enables LLMs to interact with Redis key-value stores through a set of standardized tools.

Read-only database access with schema inspection

Open source SQL-native query and provisioning engine for cloud and SaaS infrastructure. Ships as a single signed binary with MCP tools for provider discovery, schema exploration, queries, and lifecycle operations.

Generate realistic, FK-consistent test data for your databases from your AI assistant. List projects, get schema DDL, generate datasets as SQL.

Find leads, enrich your contacts, and verify emails just by describing what you need. Versium REACH builds and sizes B2B and B2C audiences and fills in the contact and company data you're missing, all in plain language with no manual exports or API code. US data only. Estimates are free; building a list draws on your Versium account credits and always confirms with you first. Requires an active Versium REACH subscription with API access.

Coding power tools for AI agents. 18 deterministic MCP tools: code-graph search, SAST, blast radius, wire-level PII redaction, signed audit chain. Works with Claude Code, Codex, Gemini, Cursor. Free tier on install.

Persistent semantic memory for SQL databases (Postgres, MySQL, MSSQL, SQLite). Annotate once, remember forever.

MCP server for @mostajs/orm: generate type-safe EntitySchemas, lint them (24 rules) and produce SQL migrations — for one ORM API across 13 databases.

Business management platform controlled entirely through conversation. Describe your business and AI builds your complete system — tables, views, dashboards, reports, chatbot — in 2 minutes. 40+ MCP tools, 26 industry templates (CRM, restaurant, clinic, hotel, school, dairy farm, and more). Free, multi-language (ES/EN/FR). Remote server with OAuth 2.1 + PKCE.

Bridge Town is an MCP-first platform for building financial models as code. Connect any agent: Claude.ai, Codex, Opencode, Cursor. Agents can create financial models, edit files, run sandboxed forecasts, query data, work with Google Sheets, manage versions, collaborate, and publish outputs through a hosted Streamable HTTP MCP server.

TypeScript stdio MCP server for operating Docker-based local-ydb deployments via local or SSH-backed profiles. Supports bootstrap, diagnostics, auth hardening, schema permissions, storage workflows, dump/restore, upgrades, and plan-first mutating operations.

Curated by Archimedes Market with a verified Trust Report (security, quality, license, complexity). Read-only PostgreSQL analytics agent exposed as an MCP server. Inspect query plans, surface slow queries, audit index usage, detect table bloat, and report vacuum status — without any DDL, DML, or write access. Designed to drop into a production database safely. → Full Trust Report: archimedes.market/assets/aa632521-f830-4adf-9828-7c6079e06a83

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.*

A Model Context Protocol (MCP) server that gives AI agents direct access to MySQL databases even on production environments.

Database-agnostic MCP server that exposes relational databases as governed business objects (Customers, Orders, Products) for AI agents via ORM abstraction — instead of raw SQL or schema access. Works with any JDBC-compliant database (PostgreSQL, MySQL, Oracle, SQL Server, SQLite, and more). Reduces LLM token consumption by 60-70% through semantic data abstraction.

**Myriade** is the AI-native data platform for exploring, cataloging, and governing data warehouses. This MCP server exposes Myriade's data analyst capabilities to any compliant client (Claude Desktop, Cursor, VS Code, Copilot, etc.). Once connected, your AI client can: - **Discover** — `list_databases`, `list_tables`, `get_table_schema`, `get_database_info` - **Sample & query** — `sample_data`, `query` (read-only, governance-enforced) - **Browse the catalog** — `search_catalog`, `read_asset`, `update_asset` (descriptions, tags, status) - **Ask the analyst** — `ask` runs Myriade's data analyst agent end-to-end on a natural-language question, returning a synthesized answer with deep links into the Myriade UI for queries, charts, and documents All requests are scoped to the authenticated user's organization, run in read-only mode, and respect Myriade's governance policies (PII masking, table-level ACLs). **Auth:** OAuth 2.1 with Dynamic Client Registration. Sign in once with your Myriade account; tokens are scoped to your org. **Self-host:** Available — see https://github.com/myriade-ai/myriade-private for setup. The hosted version at `app.myriade.ai` is the easiest way to try it.

Comprehensive Korean movie and TV database with Nielsen Korea episode ratings, KOBIS box office, MyDramaList tags, JustWatch streaming, and 5 award ceremonies. 17 tools, 10 data sources.

Airtable is the database and operations layer for your agents — whether running product, marketing, sales, ops, HR, or a custom business app. It combines structured data with multiplayer visual surfaces (grid, kanban, calendar, gallery, timeline) humans and agents share — plus sync integrations to Jira, Salesforce, Zendesk, Google Drive, Databricks, and the rest of your stack, all backed by enterprise governance. This plugin makes Claude fluent in Airtable: creating bases and schema, working with records, and sharing UI for collaboration. Bundles the official Airtable MCP server.

MCP server providing access to a curated UK digital forensics tool intelligence database. Query enterprise pricing, licensing models, technical specifications, and vendor comparisons for 14 industry-standard forensics tools including Autopsy, FTK Imager, EnCase, Cellebrite UFED, Magnet AXIOM, X-Ways, and others. Updated Q2 2026. Supports pricing_query, spec_lookup, and vendor_compare tools. Ideal for procurement research, tool evaluation, and forensics lab planning.

QueryBear is a managed MCP server that gives AI agents like Claude, Cursor, Claude Code, and Windsurf safe, read-only access to your PostgreSQL and MySQL databases. Every query runs through a security pipeline (SQL parser, allowlisted tables, blocked columns, row limits, timeouts). Audit log on every query. Column-level access controls let you hide PII, tokens, and secrets from the agent entirely. Natural-language-to-SQL with schema context. One URL, ~60-second setup, OAuth 2.1 auth — no config files, no self-hosting.

WhoDB CLI - a powerful database management tool with interactive TUI, programmatic commands, and MCP server for AI assistants.

Give AI agents structured database intelligence. Deterministic SQL, NULL trap detection, EXPLAIN pre-flight. MIT licensed.

The Caspio MCP Server allows you to connect your Caspio account to AI assistants so you can query, insert, update, and delete records using natural language. Follow the steps below to create an API profile, configure data permissions, and connect from your AI tool.

Connect Cursor, Claude Desktop, VS Code, and Windsurf to your PostgreSQL database. Per-table permission controls, encrypted credentials, and a full query audit trail. OAuth authentication, free tier available.

Strata is a self hosted AI memory server. Your AI remembers everything across every session, on your own hardware. Key features: . Semantic search (find memories by meaning, not keywords) . Per-agent API keys with granular permissions . 3D constellation viewer with live agent activity . File vault (attach real documents to memories) . CSV audit log (full transparency on every agent action) . Pre-Strata history import (your memory doesn't start at install day) . Global MCP kill switch (emergency brake, only a human can undo) . Automatic deduplication . 10 structured thought types . Backend using PostgreSQL . Runs on a Raspberry Pi Always on, always local, always yours.

Connect your real-time database to Cursor/Claude safely.Supports PostgreSQL & SQLite, built-in SQL safety validator, local-first.

Give AI agents a visual architecture canvas — build, read, and modify system design diagrams from Claude Code, Cursor, or any MCP client.

Give AI coding agents a visual architecture canvas — build, read, and modify system design diagrams

1 MCP server, 13 databases, zero config. Connect AI agents to PostgreSQL, MySQL, MongoDB, SQLite, Oracle, SQL Server, DB2, SAP HANA, MariaDB, CockroachDB, Cloud Spanner, HSQLDB, Sybase. 15 tools per entity + 4 AI prompts. Auth + RBAC included. 11 transports: REST, GraphQL, gRPC, WebSocket, SSE, tRPC, MCP, OData, Arrow Flight, JSON-RPC, NATS.

Apify-hosted MCP server for Airtable with 15 tools. Full CRUD for records, tables, fields, search, and schema inspection. No local setup needed.

Generate realistic relational test data in one API call. 156 field types, 22 locales, foreign key references across tables, JSON/CSV/SQL output. Seed your database with production-quality fake data.

MCP server for 1C:Enterprise. Metadata inspection, BSL code search, query execution, event log, syntax reference. One Go binary, zero dependencies.

A powerful Model Context Protocol (MCP) server using gemini embedding 3 that transforms any local directory into an ultrafast, visually-aware spatial search engine for AI agents.

Transform 17 source types (docs, GitHub repos, PDFs, videos, Jupyter, Confluence, Notion, Slack/Discord) into AI-ready skills and RAG knowledge. 35 MCP tools for scraping, packaging, and exporting to vector databases. Supports 16+ LLM platforms.

Talk to your databases in natural language. Multi-database MCP server (PostgreSQL, MySQL, SQLite) with 19 tools for connection management, schema introspection, query execution, rollback snapshots, and query history. Auto-discovers schema with FKs so AI agents can build JOINs from natural language. Confirmation required for destructive ops. Zero config.

Bidirectional DLP scanning for every MCP tool call — secrets, PII, prompt injection, and malicious code caught in both directions. Route all your agents (Claude, ChatGPT, Gemini, Cursor) through one endpoint with unified audit logging. Memory Vault for persistent cross-agent context. Bug Vault with 8.6M+ coding patterns across 48 languages. Free tier with 1,000 requests/day. Team RBAC and tool-level DLP policies coming March 2026.

MCP server for Manticore Search — enables AI assistants to query and manage Manticore Search databases directly. Supports full-text search, KNN vector search, fuzzy search, and real-time data access.

MCP server for Valkey and Redis observability. Query real-time health, slow commands, hot keys, anomaly detection, and cluster analytics directly from your AI assistant.

Managed MCP gateway for PostgreSQL, MySQL, MS SQL Server, and Supabase. Add your database credentials, get one secure HTTPS URL, paste it into Claude Desktop or any MCP client. No local setup, no config files, no .env management. Read-only by default, AES-256 encryption, full audit logging.

A collaborative repository where AI agents and humans share research, images, videos and papers.

MCP server for multi-database PostgreSQL analytics. Ask your databases questions in natural language - get schema exploration, profiling, trends, anomaly detection, and cross-database comparisons. Read-only, safe, local.

Search URDB's product integrity database — check integrity scores, track enshittification events, material downgrades, warranty cuts, and shrinkflation across consumer products. Sourced and evidence-backed data.

Your AI builds real data apps. You just describe the data. Chrysai gives LLMs the power to design schemas, create databases, and manage data through the MCP protocol. Describe what you want to track in plain English — your AI handles the rest.

What Staticbot does Staticbot is a managed operations platform for complex, long-running deployment workflows. It hosts static websites and web apps inside your own AWS account using S3 and CloudFront — no vendor lock-in, no PaaS markup, full ownership of the infrastructure. Core workflows: Deploy to AWS — Static websites and web apps to S3 + CloudFront CDN, with custom domains and automatic SSL via ACM Supabase migration — Orchestrates moving a full Supabase project (database, edge functions, storage, auth) to your own AWS infrastructure Multi-stage deployments — Dev, preview, and production stages per website with independent lifecycle management Staticbot owns the things agents handle poorly: state that persists across sessions, credentials that should never appear in a context window, approval gates before destructive operations, and accumulated operational knowledge from real-world failure modes.

## Overview This is an MCP (Model Context Protocol) server that provides intelligent analysis, documentation, and complete CRUD operations for PostgreSQL databases. It combines deterministic schema extraction with AI-powered reasoning and secure data manipulation operations to help users and AI agents understand and interact with complex database structures. **Performance Optimized & Extended:** Started at 38 tools, optimized to 19 tools (~50% reduction), then strategically extended to 27 tools with high-value query optimization, data management, transactions, and monitoring capabilities. ### Key Features - **Comprehensive Coverage**: 27 carefully designed tools covering all PostgreSQL operations - **Schema Extraction**: Automatically extract tables, columns, relationships, and constraints - **Intelligent Analysis**: Detect junction tables, implicit relationships, and suggest optimal joins - **AI-Powered Insights**: Leverage Ollama/LLM to generate business explanations and recommendations - **Complete CRUD Operations**: Unified tools for all data manipulation with SQL injection prevention - **Query Optimization**: Execution plan analysis, combined index analysis (suggest + unused detection) - **Data Management**: Import/export (CSV/JSON/SQL), full-text search - **Transaction Support**: Atomic multi-operation transactions with rollback - **Monitoring**: Database statistics, cache metrics, slow queries, connection tracking - **Multiple Output Formats**: - Mermaid ER diagrams (with SVG rendering) - Mermaid relationship flowcharts (with SVG rendering) - Comprehensive Markdown documentation - Visual diagram files (SVG, PNG, PDF) - **Query Assistance**: Smart join type recommendations (INNER vs LEFT) - **Modular Architecture**: Clean, extensible design organized by capability - **Diagram Rendering**: Auto-generate visual database structure diagrams - **Security**: Parameterized queries, input validation, SQL injection prevention

Give your AI agents real-time access to Dataverse — query records with OData & FetchXML, manage metadata, inspect solutions, run batch operations, audit changes, and more. Zero-config auth via Microsoft device code flow.

AI-native Postgres + REST API + auth + storage + static sites + serverless functions. Pay with x402 USDC on Base. No humans, no signups.

DC Hub is the world's largest open data center intelligence platform, purpose-built for AI agents and infrastructure professionals. Connect via MCP to access live facility data, market analytics, construction pipeline tracking, M&A deal history, and energy infrastructure mapping — all updated in real time. 7 AI platforms actively use DC Hub including Claude, ChatGPT, Gemini, Copilot, Perplexity, Grok, and DeepSeek — with 99,000+ cumulative queries through the agent registry. No authentication required for core tools. Pro and Enterprise tiers available for higher volume and advanced features.

Deploy, manage, and scale full-stack applications directly from your AI assistant. CreateOS MCP gives you 75+ tools covering project creation, GitHub/Docker/file deployments, environment management, databases (PostgreSQL, MySQL, Kafka, Valkey), security scanning, compute management, template marketplace, and billing — all from a single MCP endpoint.

AI-native cloud platform with 218 MCP tools. Deploy web services, manage PostgreSQL, MySQL, MongoDB, Redis, Meilisearch, and S3 storage from your AI agent.

Enterprise-grade (40m+ lines) codebase intelligence in a zero-setup, private and local MCP: managed indexing, hybrid semantic search, polyglot code dependency graphs, and DB/API/infra knowledge. Benchmark: 61% less tokens, 84% fewer calls, 37x faster than standard AI grep.

eip-mcp connects AI assistants to the Exploit Intelligence Platform via the Model Context Protocol. 17 tools to search vulnerabilities, analyze exploits, audit tech stacks, and generate pentest findings — with real-time data from NVD, CISA KEV, VulnCheck KEV, InTheWild.io, ENISA EUVD, EPSS, ransomware attribution, ExploitDB, Metasploit, GitHub, and more.

Search, explore, and analyze hundreds of datasets from the Nova Scotia government's datasets

Hierarchical memory for AI agents. Three-layer (buffer/working/core) with automatic decay, promotion, and semantic search.

Search 400K+ SaaS companies from any AI assistant using natural language. Free, no authentication required. Filter by 50+ data points including category, country, technology, traffic, growth rate, pricing, and more.

Exasol MCP server. Provides knowledge about the Exasol database to an LLM through the Model Context Protocol. With Exasol’s MCP Server, you’re not just giving LLMs access to your data. You’re giving them context, structure, and governance, without sacrificing performance or control.

MCP server that gives AI assistants direct access to PostgreSQL and ClickHouse databases. Supports stdio and HTTP transports.

Hypergraph database for AI agents. Store n-ary relationships (edges connecting 2+ nodes), track provenance (source and confidence), and query paths — all backed by local SQLite with zero configuration.

Turn AI conversations into live apps. Describe what you need, and AppDeploy deploys a real, full-stack app. From idea to live app in minutes, without touching infrastructure.

The pgEdge Postgres Model Context Protocol (MCP) server enables SQL queries against PostgreSQL databases through MCP-compatible clients like Claude Desktop. The Natural Language Agent provides supporting functionality that allows you to use natural language to form SQL queries. Key Features: 🔒 Read-Only Protection - All queries run in read-only transactions 📊 Resources - Access PostgreSQL statistics and more 🛠️ Tools - Query execution, schema analysis, advanced hybrid search (BM25+MMR), embedding generation, resource reading, and more 🧠 Prompts - Guided workflows for semantic search setup, database exploration, query diagnostics, and more 💬 Production Chat Client - Full-featured Go client with Anthropic prompt caching (90% cost reduction) 🌐 HTTP/HTTPS Mode - Direct API access with token authentication 🖥️ Web Interface - Modern React-based UI with AI-powered chat for natural language database interaction 🐳 Docker Support - Complete containerized deployment with Docker Compose 🔐 Secure - TLS support, token auth, read-only enforcement 🔄 Hot Reload - Automatic reload of authentication files without server restart

An enterprise-grade Oracle Database connector for MCP. Features built-in self-healing connection retries, TypeScript-based architecture, and AI-guided SQL error handling for autonomous agent workflows.

The Jotform MCP server provides programmatic access to forms and submissions via natural language interfaces, compatible with popular AI assistants including Cursor and VS Code extensions. Implementation follows the open Model Context Protocol specification, ensuring interoperability with any MCP-compatible client. The service is actively being optimized based on real-world usage patterns.This MCP is actively maintained and more tools will come soon.

Mysql_mcp_server_ddz

MCP server with 14 tools for PostgreSQL database operations. Query databases, explore schemas, analyze tables, list indexes/constraints, and get performance statistics. Features security-first design with SQL injection prevention and read-only mode by default. Works with Claude Desktop, Claude Code, and any MCP-compatible client.

Co-create schemas with your AI — typed fields, relationships, SQL-like constraints. Turn unstructured content into a curated database built for insights. Platform integrations such as PostHog and Microsoft built-in and expanding. Any HTTP API via OpenAPI import. One MCP connection gives your agent full CRUD, semantic search, and a structured knowledge base that grows correctly over time.

车辆数据库的mcp

MCP server enables AI agents to interact with MSSQL.

The Tiger MCP server provides mcp tools for managing and querying Postgres database services on tiger cloud. Tiger cloud is the best PostgreSQL cloud provider for Developers, Devices, and Agents. It provides TimescaleDB, which optimizes Postgres for insert-heavy workloads like time-series, events, transactions, and append-mostly datasets.

AI-optimized PostgreSQL expertise for coding assistants. pg-aiguide helps AI coding tools write dramatically better PostgreSQL code. It provides: - Semantic search across the official PostgreSQL manual (version-aware) - AI-optimized “skills” available through MCP tools — curated, opinionated Postgres best practices used automatically by AI agents - Extension ecosystem docs, starting with TimescaleDB, with more coming soon

A lightweight MCP (Model Context Protocol) server built to query Postgres database. It provides comprehensive tools for database operations, table introspection, and query execution with smart permission handling.

零负担、开箱即用的模型上下文协议（MCP）服务器，用于与 MySQL 交互和自动化操作。无需 Node.js 或 Python 环境。该服务器提供对 MySQL 数据库和表进行 CRUD 操作的工具，以及只读模式以防止意外的写入操作。您还可以通过添加 --with-explain-check 标志让 MCP 服务器在执行查询前使用 EXPLAIN 语句检查查询计划。

**Altinity MCP Server** is a production-ready MCP server designed to empower AI agents and LLMs to interact seamlessly with ClickHouse. It exposes your ClickHouse database as a set of standardized tools and resources that adhere to the MCP protocol, making it easy for agents built on OpenAI, Claude, or other platforms to query, explore, and analyse your data. ### Why use this server? * Seamless AI-agent integration: Designed so that agents built using OpenAI can call your database as if it were a tool. * Flexible transport support: STDIO for local workflows, HTTP for traditional REST-style calls + streaming support via SSE for interactive flows. * Full tooling and protocol support: Built-in tools for schema introspection, SQL execution, resource discovery. * Security and enterprise-grade: Supports JWE/JWT authentication, TLS for ClickHouse connection and MCP endpoints. * Open-source and extensible: You can customise, extend, embed into your stack. ### Key Features * **Transport Options**: * **STDIO**: Run locally via standard input/output — ideal for embedded agents or local workflows. * **HTTP**: Exposes MCP tools as HTTP endpoints, enabling Web, backend, agent access. * **SSE (Server-Sent Events)**: Enables streaming responses — useful when you want the agent to receive chunks of results, respond interactively, or present live data. * **OpenAPI Integration**: When HTTP or SSE mode is enabled, the server can generate a full OpenAPI specification (v3) describing all tools and endpoints. This makes it easy for OpenAI-based agents (or other LLM platforms) to discover and call your tools programmatically. * **Security & Authentication**: Optional JWE token authentication, JWT signing, TLS support both for the MCP server and the underlying ClickHouse connection. * **Dynamic Resource Discovery**: The server can introspect the ClickHouse schema and automatically generate MCP “resources” (tables, views, sample data) so agents understand your data context without manual intervention. * **Configuration Flexibility**: Configure via environment variables, YAML/JSON configuration file or CLI flags. Includes hot-reload support so you can adjust config without full restart. ### Use-Cases * AI assistant integrated with OpenAI: For example, you build an agent using OpenAI’s API which reads your schema via the OpenAPI spec, selects the right tool, calls the HTTP/SSE endpoint of the MCP server, and returns analytic results to the user. * Streaming analytics: Large result sets, or interactive analytics flows, where SSE streaming gives progressive results, keeps your UI or agent responsive. * Secure enterprise access: Instead of giving agents full DB credentials, you expose via the MCP server with fine-grained auth, limit enforcement, TLS, and tool-level control. * Schema-aware LLM workflows: Because the server exposes table and column metadata and sample rows as resources, the LLM can reason about your data structure, reducing errors and generating better SQL or queries.

Integrate your Alkemi Data, connected to Snowflake, Google BigQuery, DataBricks and other sources, with your MCP Client.

The server provides context to an LLM, empowering models to complete various Database Administration tasks, such as improving table health, optimizing existing indexes, and identifying missing ones. The server aims to find the best way to provide context so the LLMs can maximize their potential in completing DBA tasks.

A comprehensive Model Context Protocol (MCP) server for SeaTable that exposes end‑to‑end database capabilities (schema introspection, CRUD, querying, linking, select option management, and file attachment stubs) through 18+ rigorously defined tools. You can run it: - As a local CLI (stdio) MCP server - As an HTTP SSE server for local or remote IDEs - As a globally distributed Cloudflare Worker (dual transport: `/sse` + `/mcp`) Version 1.0.3 introduces fully explicit Zod schemas for every tool in the Cloudflare Worker path, eliminating earlier adapter ambiguity and ensuring reliable argument delivery across hosts.

Guepard is an agent-native data platform that brings Git-style versioning to databases. It enables instant branching, safe snapshots, and full time-travel capabilities so developers and AI agents can explore, test, and collaborate on data without fear of corruption or downtime.

With Guepard MCP, managing data is no longer a chore. It becomes fast, reliable, and as intuitive as writing an idea down.

A Model Context Protocol (MCP) server that leverages a vector database to efficiently index and query the codebase.

A Model Context Protocol (MCP) server for Querysharp app, enabling AI assistants to help you analyze and optimize your PostgreSQL database performance directly from your code editor.

Claude Context is an MCP plugin that adds semantic code search to Claude Code and other AI coding agents, giving them deep context from your entire codebase.

The CockroachDB MCP Server is a natural language interface designed for agentic applications to manage, monitor and query data in CockroachDB.

Endor lets your AI agents run services like MariaDB, Postgres, Redis, Memcached, Alpine, or Valkey in isolated sandboxes. Get pre-configured applications that boot in less than 5 seconds, with direct AI agent integration for instant development and testing environments.

An MCP server for Milvus that supports session connection management and accesses Milvus via the SSE interface.

MCP Toolbox for Databases is an open source MCP server for databases. It enables you to develop tools easier, faster, and more securely by handling the complexities such as connection pooling, authentication, and more.

A comprehensive Model Context Protocol (MCP) server for SQL Server database operations. This server provides 10 powerful tools for database analysis, object discovery, and data manipulation through the MCP protocol.

A comprehensive Model Context Protocol (MCP) server for accessing the PubChem chemical database. This server provides access to over 110 million chemical compounds with extensive molecular properties, bioassay data, and chemical informatics tools.

A Model Context Protocol (MCP) server seamlessly connecting AI Agents and AI coding tools with Zilliz Cloud https://zilliz.com/

A Model Context Protocol (MCP) server seamlessly connecting AI Agents and AI coding tools with Zilliz Cloud https://zilliz.com/

A database server based on Model Context Protocol (MCP), supporting SQLite, MySQL, PostgreSQL, and MSSQL databases.

Allows to query and inspect Vertica databases

An openGauss database MCP server build with FastMCP, Supporting Stdio, SSE and Streamable-Http transporting mode, 2 resources: get_schemas, get_tables 3 tools: execute_query, get_table_definition, list_tables_in_current_schema, get_current_user_and_schema Can read and write openGauss database.

Memory-enhanced MCP server with local RAG database and expiring memory capabilities

MCP (Model Context Protocol) server for Microsoft SQL Server integration built with NestJS

MCP Server for DuckDB

Bio MCP server that deals with longevity databases (open-genes, genage and so on)

MCP server for a school database

Leverages your Schemas and Access Patterns to interact with your DynamoDB Database using natural language.

Local dynamic mcp server for FileMaker databases

MCP server for reading SQLite databases

Test of Python MCP server implementation for Supabase

A Model Context Protocol (MCP) server for agentic retrieval and semantic search over unstructured and structured data using Milvus, a high-performance vector database. This server enables large language model (LLM) applications to efficiently index, store, and retrieve vector embeddings derived from diverse data sources—such as documents, images, metadata, or logs. By leveraging Milvus, the MCP server supports similarity search and contextual retrieval, enabling intelligent access to relevant information based on natural language queries. Designed for integration with MCP-compatible clients, this solution provides a scalable, low-latency foundation for building AI-powered applications with contextual awareness and retrieval-augmented generation (RAG) capabilities.

MCP Server for Turkish Thesis Database

Secure MCP server for libSQL databases with comprehensive tools, connection pooling, and transaction support. Built with TypeScript for Claude Desktop, Claude Code, Cursor, and other MCP clients.