kubectl-ai: Your AI-Powered Kubernetes Assistant 🤖

MCP server for freshjots.com

Minimal project management for teams and AI agents. Manage tasks, projects, and workspaces from Claude, Cursor, Windsurf, and any MCP-compatible AI assistant.

Recruiting-ops MCP for asking TalentLyft questions and finding bottleneck owners inside Claude, Cursor, or another AI workspace.

百度地图核心API现已全面兼容MCP协议，是国内首家兼容MCP协议的地图服务商。

Recruiting-ops MCP for asking Factorial ATS questions, inspecting webhook-backed sync setup, and finding bottleneck work inside Claude, Cursor, or another AI workspace.

Web content fetching and conversion for efficient LLM usage

Pre-install trust scoring & safety scanning for MCP servers, AI skills & npm packages — 15 signals incl. OSV/KEV/EPSS vuln intel and an auto-gate go/no-go.

# 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)

Repository management, file operations, and GitHub API integration

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"

Browser automation and web scraping

Conecta tus asistentes de IA (Claude, Cursor, Cline, etc.) con **CLI MARKET**, la plataforma de marketplace para línea de comandos.

Location services, directions, and place details

Read-only database access with schema inspection

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

AI Governance Infrastructure for Microsoft 365. 24 MCP tools with policy enforcement, Need-to-Know compliance filter, revision-safe audit trail, and per-resource scope control. Deployed in your own Azure tenant — your data never leaves. DSGVO-first, EU AI Act ready.

A Model Context Protocol (MCP) server that integrates with Jina AI Search Foundation APIs.

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

clariBI is an AI-powered business intelligence platform built for small and mid-sized SaaS, ecommerce, and operations teams. Instead of writing SQL or building dashboards manually, users ask questions in plain English ("what's our CAC by channel last quarter?") and the AI engine pulls the data from Stripe, HubSpot, Google Analytics, ad platforms, Jira, and 30+ other sources via the open Model Context Protocol. Dashboards generate themselves on source connect.

An unofficial MCP Server for PrimeReact 10.x to fill the gap while we wait for the official one (coming with PrimeReact v11). **What it does:** - Covers 105 PrimeReact v10 components parsed from their TypeScript definitions - Exposes props, types, JSDoc descriptions, and event callbacks - Tools include: `get_component`, `get_component_props`, `search_components`, `suggest_component`, `get_form_components`, `get_data_components`, and more **Why it's useful:** If you use Claude Code, Cursor, or any MCP-compatible AI assistant, you can now get accurate PrimeReact component suggestions and code generation grounded in the actual API — no more hallucinated props.

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.