MCP Servers Directory

MCP server registry and hosting

Registry and management platform for Model Context Protocol (MCP) servers that helps teams securely discover, install, deploy, and connect MCP servers for AI assistants. Smithery combines a searchable catalog, CLI setup, hosted deployments, namespaces, connection APIs, and scoped service-token flows for clients such as Claude, Cursor, Windsurf, and Codex.

MCP server directory and gateway

AI gateway and model management platform that provides a unified API for accessing multiple LLM providers (OpenAI, Anthropic, Google, Mistral, and more) through a single endpoint. Features model routing, cost tracking, usage analytics, rate limiting, and API key management across providers. Simplifies multi-provider AI integration by handling authentication, billing, and failover in one place. Useful for teams evaluating multiple models or building provider-agnostic AI applications.

Hosted MCP server platform

Platform for running MCP servers securely in WebAssembly sandboxes. One-click install for Claude Desktop, VS Code, and other MCP-compatible clients with no Docker required. Simplifies MCP server deployment and management by handling isolation, security, and distribution, making it easy to extend AI assistants with custom tools without infrastructure overhead.

Official MCP server for GitHub repo operations

GitHub MCP Server is the official Model Context Protocol server from GitHub that connects AI assistants to repositories, issues, pull requests, workflows, and code search. It exposes 100+ operations with toolset filtering, permission scoping, and audit logging, available in both remote-hosted and self-hosted Docker deployment modes.

Official open catalog of Model Context Protocol servers

The official, community-run registry for discovering and publishing Model Context Protocol (MCP) servers — an open index that MCP clients read to find available servers.

Up-to-date docs for AI code editors via MCP

Context7 is an MCP server developed by Upstash that injects up-to-date, version-specific documentation directly into AI code editors and coding assistants. By typing 'use context7' in prompts, developers get accurate library documentation instead of hallucinated or outdated API references. It pulls from official source documentation and serves it through the Model Context Protocol, solving the common problem of LLMs generating code with incorrect or nonexistent API calls.

Apache-2.0 Python framework for repeatable LLM, RAG, agent, MCP, and safety evaluation workflows.

DeepEval is an Apache-2.0 Python framework for evaluating LLM apps, RAG systems, agents, MCP workflows, and safety behavior with repeatable test cases. It works locally and in CI/CD, then connects to Confident AI for hosted reports, observability, red teaming, and governance when teams need shared evidence instead of ad-hoc prompt reviews and manual QA.

Turn websites into LLM-ready structured data

Firecrawl is a Y Combinator-backed API that crawls websites and converts them into clean, LLM-ready Markdown or structured JSON. Handles JavaScript rendering, pagination, sitemaps, and anti-bot measures automatically. Designed for RAG pipelines, AI agents, and data extraction workflows. Features batch crawling, scheduled scraping, webhook notifications, and custom extraction schemas. Processes content for direct ingestion into vector databases and LLM context windows.

Virtual engineering team as Claude Code skills by YC CEO Garry Tan

GStack transforms Claude Code into a structured virtual engineering team through 23 opinionated slash command skills created by Y Combinator CEO Garry Tan. It assigns specialist roles including CEO product review, engineering manager architecture oversight, designer visual audit, QA lead with real browser testing, and release engineer deployment. Each skill enforces focused workflows with clear decision principles for running parallel coding sessions.