About 🔍 mcp-find
mcp-find is a command-line tool for developers to discover MCP servers through mcprepository.com’s public search API. It can run with npx or be installed globally.
Key features
- 01Search MCP servers by keyword
- 02Query the public index API
- 03Show descriptions and links in the terminal
- 04Run with npx or install globally
Use cases
- 01Discover servers for an agent project
- 02Find Slack-related servers from a terminal
MCP support
The tool searches an MCP server directory; it is not a client for connecting to or managing servers. It retrieves results through mcprepository.com’s public search API.
Configuration
Local server config is the same for every client. Copy it into your client’s MCP config file.
Tools
Local servers expose tools only once running on your machine, and the maintainer has not provided a tool list.
Details
From the project README🔍 mcp-find
Search for MCP Servers right from your terminal.
Powered by mcprepository.com — the official MCP Server Repository.
📦 About
mcp-find is a simple CLI tool that helps you discover MCP Servers (Model Context Protocol) using the public search API from mcprepository.com.
Whether you're building AI agents, automation tools, or protocol-based applications — quickly find the MCP Server you need.
🚀 Installation
You can use it instantly via npx:
npx mcp-find <search-term>
Or install globally:
npm install -g mcp-find
🔍 Usage
mcp-find slack
Example output:
🔍 Searching for MCP servers matching: "slack"...
slack — Slack MCP Server: ★ 28.3k — MCP server for interacting with Slack
→ https://mcprepository.com/modelcontextprotocol/slack
slack-user-mcp — Slack User MCP Server: ★ 3 — MCP server for interacting with Slack as a user
→ https://mcprepository.com/lars-hagen/slack-user-mcp
🌐 Powered by MCPRepository
This tool uses the official MCP Server index API:
GET https://mcprepository.com/api/search?q=<query>
Comments
New to MCP?
From what the protocol is to installing your first server, these answers cover most first questions.
An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.