Model Context Protocol (MCP) servers connect AI agents to external tools, APIs, and data sources through a single open standard, so you wire up a capability once and any MCP-compatible client can use it. Introduced by Anthropic in late 2024 and now supported across the major agent tools, MCP is becoming the USB-C of AI integrations. This is the index of the most useful MCP servers for building with agents — official and community alike.
MCP gateway that gives AI agents like Claude Code, Cursor, and Codex one endpoint and one tool shape for every integration, with sandboxed execution and credential isolation by design.
Context compression MCP server and proxy for AI agents, cuts token usage 60-95% across tool outputs, logs, RAG, and files while preserving answer accuracy.
Best for Agentic workflows drowning in tool call outputs, logs, RAG results, and conversation history that inflate token costsMCP is an open protocol built on JSON-RPC. An MCP server exposes three kinds of capability to a client: tools (actions the model can call, like querying a database or opening a PR), resources (read-only context such as files or records), and prompts (reusable templates). Clients connect over a local transport (stdio) or a remote one (Server-Sent Events or streamable HTTP), which means a server can run on your machine or as a hosted service.
The payoff is portability: the same server that gives Claude access to your Postgres database also works in Cursor, VS Code, Windsurf, Zed, and other MCP clients, with no bespoke integration per tool. Because servers can read data and take actions, run ones you trust, prefer official or open-source implementations, and use OAuth or scoped credentials where a server supports it. Each entry below links to its repository with supported clients and exact setup.
Model Context Protocol (MCP) is an open standard for connecting AI agents to tools and data. An MCP server exposes a capability — a database, an API, a filesystem — that any MCP-compatible client can discover and call through one common protocol.
MCP uses JSON-RPC between a client (the AI app) and a server (the capability). A server offers tools the model can invoke, resources it can read, and prompt templates, communicating over stdio for local servers or SSE/HTTP for remote ones. The client handles discovery and passes the model's requests to the server.
Claude (desktop, Code, and the API), Cursor, VS Code, Windsurf, Zed, and a growing list of agentic clients support MCP. Check each server's link for the clients it has been tested with.
You register the server in your client's MCP configuration — typically its name, command or URL, and any credentials — then restart or reload the client so it picks up the new tools. Each entry links to its repository with exact configuration.
An API is the raw interface a service exposes; an MCP server wraps a capability in the MCP standard so any agent can use it without custom code. A plugin is usually tool-specific, while an MCP server is portable across every MCP client.
MCP servers can read data and take actions, so treat them like any dependency: run servers you trust, prefer official or open-source ones you can inspect, scope credentials narrowly, and use OAuth where the server supports it. Review what tools a server exposes before connecting it.
Official servers are published by the company behind the service (for example, a vendor's own database server); community servers are built by third parties. Both can be excellent — Analog curates for quality and labels the source on each entry.