mneme is an open-source, vault-native memory layer for Claude Code and any MCP client. Your Markdown files are the source of truth. The index, embeddings, and knowledge graph are all derived and rebuildable. No LLM or network on the capture path. Redaction runs before every derived store. MIT licensed.

Flag features directly from chat in your code editor, including VS Code, Cursor, Windsurf, Claude Code—any IDE with MCP support.

BlenderMCP connects Blender to Claude AI through the Model Context Protocol (MCP), allowing Claude to directly interact with and control Blender. This integration enables prompt assisted 3D modeling, scene creation, and manipulation.

基于Anduin2017 / HowToCook （程序员在家做饭指南）的mcp server，帮你推荐菜谱、规划膳食，解决“今天吃什么“的世纪难题； Based on Anduin2017/HowToCook (Programmer's Guide to Cooking at Home), MCP Server helps you recommend recipes, plan meals, and solve the century old problem of "what to eat today"

Multi-perspective decision reasoning for Claude. When you face a real choice: career, money, relationships, strategy. Orlog examines competing angles and takes a clear position, not a hedge. Get an API key at orlog.fyi.

An MCP service designed for deploying HTML content to EdgeOne Pages and obtaining an accessible public URL.

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

Browser automation and web scraping

summarize chat message

Web and local search using Brave's Search API

EQVPS is crypto-native, no-KYC VPS hosting that an AI agent can rent and operate end-to-end over MCP — no human in the loop. The agent registers, gets a Bearer token, picks a plan, pays in USDC/USDT on Base/Ethereum or USDT on Tron from a prepaid balance, and the VPS provisions in ~60s. 16 tools cover the full lifecycle: discover plans, register/login, balance + top-up, order, pay invoice, list/inspect servers, power, set hostname, reset password, reinstall OS, metrics, cancel. The only VPS provider with MCP provisioning — an agent orders AND pays for its own server, no KYC, no human.

GitLab API, enabling project management

A Model Context Protocol (MCP) server for image generation and manipulation using fal.ai's Stable Diffusion model.

# govql-mcp-server An MCP (Model Context Protocol) server for [GovQL](https://govql.us) — gives AI clients like Claude Desktop, Claude Code, and Cursor direct access to the US Congressional GraphQL API at [api.govql.us/graphql](https://api.govql.us/graphql) without bespoke HTTP wiring. For the design rationale (why FastMCP-Python, the passthrough+curated philosophy, roadmap through v0.4), see [design.md](https://github.com/govql/govql/blob/main/mcp-server/docs/design.md). ## What you can do with it Ask an agent questions like: - *"How did Vermont's two senators vote on the most recent nomination?"* - *"Which legislators in the 118th Congress switched parties during their service?"* - *"Compare Senator Sanders' voting record to Senator Murkowski's on cloture votes in the most recent Congress."* The agent picks the right tool, writes the GraphQL query against the live schema, and parses the response — no manual API wrangling. ## Install The server runs as a per-client subprocess over stdio. Pick your client: ### Claude Desktop Edit `claude_desktop_config.json` (Settings → Developer → Edit Config): ```json { "mcpServers": { "govql": { "command": "uvx", "args": ["govql-mcp-server"] } } } ``` Restart Claude Desktop. The `govql` tools appear in the tools panel. ### Claude Code Add to `.mcp.json` in your project (or `~/.mcp.json` for global): ```json { "mcpServers": { "govql": { "command": "uvx", "args": ["govql-mcp-server"] } } } ``` ### Cursor Settings → MCP → Add Server. Use the same `command` / `args` as above. ### Other clients Any MCP-compatible client that supports stdio servers will work. The command is `uvx govql-mcp-server` with no required arguments. ## Tools | Tool | Purpose | |---|---| | `execute_graphql` | Run any GraphQL query against the GovQL endpoint. Returns the result plus an `last_ingest` timestamp so the agent can reason about data freshness. | | `list_types` | Returns the names and kinds of every type in the GovQL schema. Optional `kind` filter (`"OBJECT"`, `"INPUT_OBJECT"`, `"ENUM"`, etc.) to narrow further. Start here when you don't know what's queryable. | | `describe_type` | Returns one type's full details — fields, arg signatures, input fields, enum values. Call after `list_types` to learn the shape of a specific type before writing a query. | ## Configuration All env vars are optional — the package is zero-config for end users. | Env var | Default | Purpose | |---|---|---| | `GOVQL_ENDPOINT` | `https://api.govql.us/graphql` | Endpoint to query. Override to point at a local dev stack. | | `GOVQL_TIMEOUT_MS` | `30000` | Per-request HTTP timeout. | | `LOG_LEVEL` | `INFO` | Logging level. Logs go to stderr only (stdout is reserved for the MCP transport). | ## Limits (enforced by the upstream API) - Max query depth: 10 - Max query complexity: ~10 billion points (`first: N` multiplies child cost by N — keep page sizes reasonable on deeply nested queries) - Rate limit: 100 requests / 60 s per source IP A depth or complexity violation surfaces as a GraphQL `errors` entry in the tool response so the agent can adjust and retry. ## Data freshness Every `execute_graphql` response includes a `last_ingest` ISO timestamp. Vote data refreshes hourly; legislator data refreshes daily. ## Status Version 0.1.0 ships three foundational tools: a GraphQL passthrough (`execute_graphql`) and two narrow schema-discovery tools (`list_types`, `describe_type`). Curated higher-level tools (`find_legislator`, `get_voting_record`, `compare_voters`, etc.) are planned for subsequent releases — see [design.md](https://github.com/govql/govql/blob/main/mcp-server/docs/design.md) for the roadmap. ## Links - [GovQL project site](https://govql.us) - [GraphQL API](https://api.govql.us/graphql) - [Source / issues](https://github.com/govql/govql)

One API for Search, Crawling, and Sitemaps

Zhipu Web Search MCP Server is a search engine specifically designed for large models. It integrates four search engines, allowing users to flexibly compare and switch between them. Building upon the web crawling and ranking capabilities of traditional search engines, it enhances intent recognition capabilities, returning results more suitable for large model processing (such as webpage titles, URLs, summaries, site names, site icons, etc.). This helps AI applications achieve "dynamic knowledge acquisition" and "precise scenario adaptation" capabilities.

143 local tools for Claude, Cursor & ChatGPT — Mail, iMessage, Microsoft Teams, Slack, WhatsApp, Calendar, OneDrive, Outlook, OmniFocus, Notes, Finder and Office docs. 100% local, no API keys. Reaches what cloud connectors can't. Mac + Windows.

An MCP server implementation for retrieving information from the AWS Knowledge Base using the Bedrock Agent Runtime.

ead-only MCP server for searching and browsing essays on payments, fintech, and protocol thinking by Ben Milne. 3 tools: list_posts, get_post, search_posts. Streamable HTTP transport, no auth required.

MCP server to provide Figma layout information to AI coding agents like Cursor

Race nutrition planning tools for endurance athletes. Get personalised carb, sodium and fluid targets for any race, browse Lecka's real-food product catalog, and calculate exactly how many gels you need — by distance, conditions, weight, and effort level.

The Meteomatics MCP (Model Context Protocol) server allows AI agents to securely access Meteomatics weather data tools. MCP is an open standard that lets AI applications connect to external tools and data sources. With the Meteomatics MCP, agents can request weather data directly from the Meteomatics API through a secure /mcp endpoint. The MCP server manages authentication using OAuth (client registration, authorization code + PKCE, and refresh tokens). Once connected, the AI agent can access data, models, parameters, time ranges that are available to your Meteomatics API account.

Local-first, lightweight MCP server for per-agent key management.

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

An MCP server implementation that provides a tool for dynamic and reflective problem-solving through a structured thinking process.

Kage is shared, code-grounded memory for developers and their coding agents. Capture a learning once — a bug cause, a decision, a gotcha — and the whole team plus every agent recalls it next time it's relevant. Memory is grounded in your actual code and stored as git-tracked JSON reviewed in PRs, not a personal vector blob that drifts: citations are validated on write, and stale memory (whose code was deleted or refactored) is withheld from recall and flagged for you. Works with Claude Code, Codex, Cursor, Windsurf, and any MCP client. No vector DB, API key, or service to run. Try it in 30s: npx -y @kage-core/kage-graph-mcp demo

GBOX provides environments for AI Agents to operate computer and mobile devices. Mobile Scenario: Your agents can use GBOX to develop/test android apps, or run apps on the Android to complete various tasks(mobile automation). Desktop Scenario: Your agents can use GBOX to operate desktop apps such as browser, terminal, VSCode, etc(desktop automation). MCP: You can also plug GBOX MCP to any Agent you like, such as Cursor, Claude Code. These agents will instantly get the ability to operate computer and mobile devices.

Repository management, file operations, and GitHub API integration