MCPfinder is a free, open-source discovery and install layer for AI agents. It helps an assistant search multiple MCP registries, inspect trust signals and environment variables, and generate install-ready JSON for the right client.
Model Context Protocol (MCP) is an open standard by Anthropic that lets AI models connect to external tools and data sources. Think of it as USB-C for AI — a universal interface.
MCP servers give your AI agent superpowers: access databases, call APIs, manage files, interact with services — all through a standardized protocol.
MCPfinder is an MCP server that discovers other MCP servers. The primary user is the AI assistant itself; the human installs it once, then the assistant uses it repeatedly.
MCPfinder connects to your AI agent as an MCP server. Your AI can then discover candidate servers, inspect trust signals, and generate install config with minimal round-trips.
One install step. Minimal setup. Add MCPfinder as an MCP server to your AI client, then let the assistant discover and configure downstream MCP servers.
Your AI agent asks MCPfinder: "I need a tool for X." MCPfinder searches across multiple registries and returns the best match.
Your AI gains new abilities on-demand. Need a database tool? A code formatter? A web scraper? MCPfinder finds it instantly.
Choose your preferred installation method. Works with Claude Desktop, Cursor, Windsurf, and any MCP-compatible client.
Humans install MCPfinder once. Assistants use it repeatedly to discover missing capabilities, inspect trust signals, and generate install-ready config.
Find candidate servers by keyword, technology, or use case. Use this first when the user needs a capability you do not already have.
Inspect trust signals, tools, warning flags, transport, and required environment variables before recommending anything.
Generate a client-specific JSON config snippet for Claude Desktop, Cursor, Claude Code, Cline, or Windsurf.
Explore categories when the user only knows a domain like database, filesystem, communication, or security.
MCPfinder aggregates, searches, scores, warns, and generates install config — so your AI can choose tools with less guesswork.
Aggregates the Official MCP Registry, Glama, and Smithery into one unified search. Never miss a server.
Search returns confidence, recommendation reasons, and warning flags so an assistant can decide whether to recommend a server strongly or cautiously.
One install for MCPfinder itself. After that, your assistant can generate install-ready JSON snippets for the right downstream MCP server.
MCPfinder is itself an MCP server. Your AI agent discovers tools programmatically — no human browsing required.
Bootstraps from published snapshots and syncs from upstream registries. The snapshot manifest is a better freshness signal than vague marketing claims.
AGPL-3.0 licensed. Inspect the code, contribute, or fork it. Built by the community, for the community.
Snapshot freshness appears here when the manifest endpoint is reachable.
Everything you need to know about MCPfinder and MCP servers.
get_server_details, then generate config with get_install_config.
npx @mcpfinder/server or add it to your project with npm install @mcpfinder/server. No API keys or subscriptions required.
dev.mcpfinder/server.
Use the local stdio server as the canonical interface, then point your assistant at MCPfinder whenever it needs a missing capability.