SPECMEM

The new purpose-built IDE to harness magic

A standalone Python/FastAPI server that implements the Model Context Protocol (MCP) for the OPTIX threat intelligence platform. It exposes 26 analyst-friendly tools that AI assistants and programmatic consumers can use to query threat feeds, search documents and indicators, manage watchlists, triage IOCs, generate detection rules, trigger AI research, and produce intelligence reports — without needing to understand OPTIX's internal REST API.

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.

Manage your LinkaGoGo bookmarks through any AI assistant that supports the Model Context Protocol (MCP). Search, add, organize, tag, move, and export bookmarks conversationally — 16 tools for full bookmark and folder management. Connect via Claude.ai, Claude Desktop, or any MCP-compatible client.

AI GPT Image is an online GPT Image 2 AI image generator and editor that helps users create, edit, upscale, and transform images with text prompts and reference photos. It supports text-to-image generation, image-to-image transformation, natural language image editing, AI image upscaling, and AI video creation workflows. Powered by GPT Image 2, AI GPT Image is designed for high-quality visual creation with stronger prompt understanding, improved text rendering, multilingual visual design, realistic lighting, detailed textures, and better style consistency. Users can create realistic portraits, product photos, YouTube thumbnails, social media graphics, posters, UI mockups, infographics, character concepts, cinematic storyboards, and commercial design assets in just a few steps. AI GPT Image is suitable for creators, designers, marketers, e-commerce sellers, bloggers, YouTubers, startup teams, indie hackers, AI creators, and anyone who wants to generate professional images online without complex design software. It is especially useful for social media content, advertising creatives, product marketing, blog covers, visual storytelling, image editing, and AI image-to-video workflows.

⏩ Create, share, and use custom AI code assistants with our open-source IDE extensions and hub of models, rules, prompts, docs, and other building blocks

Persistent semantic memory for Claude Code, chats AND code. 74+ MCP tools, local pgvector embeddings, codebase indexing with 14 language extractors, multi-agent team coordination. Self-hosted, zero cloud dependency.

An MCP server that gives AI assistants access to your Copilot Money personal finance data. It reads from the locally cached Firestore database (LevelDB + Protocol Buffers) on your Mac. Reads are 100% local with zero network requests. 17 read tools across spending, investments, budgets, goals, and more — query transactions, accounts, holdings, balances, categories, recurring charges, budgets, goals, and investment performance. There's also write tools (optional with the flag)

MCP (Model Context Protocol) Server for Sychev Lab - provides access to products, articles, tutorials, and e-commerce features located in https://lab.sychve.xyz

Your AI Partner on Desktop

Open-source AI Agent for VS Code and JetBrains that autonomously solves coding tasks end-to-end.

Context-optimized MCP server for DTC e-commerce brands. Connect Claude (or any MCP client) to your Klaviyo and Shopify data with 16 pre-built analytics tools. Unlike raw API wrappers, dtc-mcp pre-aggregates data server-side and returns only actionable fields — using ~80% less context than dumping raw API responses into your conversation. Features 8 Klaviyo tools — campaign performance, flow breakdowns, subscriber health, profile search, event activity 7 Shopify tools — sales summaries, daily/weekly/monthly trends, product performance, inventory alerts, customer cohorts, order search 2 cross-platform tools — email revenue attribution, full DTC health dashboard Dual revenue metrics — both gross and net revenue on every sales query Smart fallbacks — ShopifyQL when available, GraphQL pagination when not Aggressive caching — respects Klaviyo's strict rate limits (1 req/s on reporting)

StationOne is a local-first, model-agnostic AI client and orchestration layer that enables users to build, run, and manage agentic workflows across multiple LLMs and MCP connectors. It provides a unified workspace for integrating AI models, tools, and data sources through the Model Context Protocol (MCP), allowing agents to securely interact with external services like APIs, databases, and SaaS platforms. With support for custom agents, pre-prompt templates, and a curated MCP marketplace, StationOne acts as a central hub for deploying scalable, context-aware AI automation across personal and enterprise environments.

# archon-mcp-client A completely silent, standards-compliant [Model Context Protocol (MCP)](https://modelcontextprotocol.io/) client bridge that natively proxies Claude Desktop `stdio` requests directly into an Archon `SSE` remote backend over HTTPS. Unlike other public MCP proxies, this package guarantees complete `stdout` JSON-RPC hygiene, preventing log pollution that crashes Claude Desktop's strict parsers. ## Usage You do not need to install this library. Just configure your `claude_desktop_config.json` to execute it remotely via `npx`. Find more information and full documentation at [https://archonspecs.dev/docs.html](https://archonspecs.dev/docs.html). ```json { "mcpServers": { "archon-mcp": { "command": "npx", "args": [ "-y", "archon-mcp-client", "https://archonspecs.dev/mcp/sse?apiKey=your_api_key_here" ] } } } ```

## Overview ### what is Chain.Love MCP? Chain.Love MCP is a hosted remote MCP server and gateway for AI agents. It provides a single endpoint for discovering and comparing Web3 infrastructure services across 50+ blockchain networks, including RPCs, indexing, oracles, storage, compute, and developer tools. ### how to use Chain.Love MCP? To use Chain.Love MCP, add the hosted endpoint to your MCP client and connect to `https://app.chain.love/mcp` over Streamable HTTP. For public use cases, the basic MCP server URL is enough. For private downstream MCPs, add credentials only when required using `x-chainlove-cred-<credentialKey>` headers. ### key features of Chain.Love MCP? - Hosted remote MCP gateway for AI agents - Single endpoint for Web3 infrastructure discovery across 50+ blockchain networks - Aggregates infrastructure options across RPCs, indexing, oracles, storage, compute, and developer tools - Streamable HTTP transport - Public documentation and onboarding resources available online ### use cases of Chain.Love MCP? - Discovering and comparing Web3 infrastructure providers across many blockchain networks - Finding RPC, indexing, oracle, storage, compute, and developer tooling options through one MCP server - Giving AI agents a single hosted integration surface for Web3 infrastructure discovery - Reducing the need to integrate many separate provider-specific endpoints ### FAQ from Chain.Love MCP? - Can Chain.Love MCP be used as a hosted remote MCP server? Yes. Chain.Love MCP is designed to be consumed as a hosted remote MCP endpoint at `https://app.chain.love/mcp`. - Does Chain.Love MCP require credentials? Not always. Some downstream integrations may require credentials, which can be passed using `x-chainlove-cred-<credentialKey>` headers when needed. - How do I know which credential header to use? You can check the open-source Chain.Love registry at `https://github.com/Chain-Love/chain-love/blob/main/references/offers/mcpservers.csv` or browse `https://app.chain.love/toolbox/mcpservers` and look for the relevant `credentialKey` value. - Where can I learn more? Landing page: `https://www.chain.love/mcp-gateway` Documentation: `https://chain-love.gitbook.io/mcp-module`