This MCP server repository serves as a hub for a collection of Model Context Protocol (MCP) servers organized under the awesome-mcp project. The listing is categorized into official, ai, analysis, and browser groups, each pointing to individual MCP server implementations (for example AWS KB Retrieval, Brave Search, Knowledge Graph Memory, Puppeteer, etc.). To use these servers, you would deploy this MCP hub alongside the individual server you want to interact with, and then query the MCP API to discover, route, and execute the appropriate tool or capability. The included servers expose common MCP capabilities such as web content retrieval, memory-backed context, file system and database access, browser automation, and more, enabling LLMs to perform tasks with specialized tooling without leaving the MCP framework. Start by selecting the target server from the list, launching it, and then using your MCP client to invoke the server’s tools via the MCP protocol (e.g., tool calls, memory access, and data retrieval).

Prerequisites:

• A runtime suitable for the chosen server command (e.g., Node.js for Node-based MCP servers or Python for Python-based servers).
• Git to clone the repository.
• Basic environment to run the MCP hub and the individual servers.

Installation steps:

1. Clone this repository (the awesome MCP hub): git clone https://github.com/aimcp/awesome-mcp.git cd awesome-mcp

2. Install dependencies (example for a Node-based setup, adjust if your environment differs): npm install

3. Configure the MCP server you want to run from the list (adjust path and command as needed based on your environment). Example placeholder configuration:

   • Ensure you have the required server files at path/to/server.js or your actual entry point.
   • If using a different runtime, adjust the command and args accordingly in the mcp_config section of this repository or in your deployment environment.

4. Run the MCP hub and the selected server: node path/to/server.js

   or if you have an entry point for the hub itself, start that as your orchestration service

5. Verify the MCP endpoint is reachable (e.g., http://localhost:PORT or the configured address) and begin issuing tool calls via your MCP client.

Notes and tips:

• The repository lists multiple MCP servers across categories (official, ai, analysis, browser). Each server may have its own API surface and capabilities. Refer to the specific server’s documentation or code for exact usage details.
• If you plan to run multiple servers, consider containerizing them (Docker) or using a process manager to keep services online and nicely logs-collected.
• Common environment variables to prepare include MCP_SERVER_NAME and MCP_SLUG to identify your hub deployment; adapt them to your hosting environment.
• If you encounter port conflicts, assign unique ports to the hub and each server, and update the MCP endpoints accordingly.
• Ensure you have network access permissions for external services (e.g., AWS KB, Google Drive, Brave Search) when the corresponding MCP servers are active.
• When updating the configuration, keep a versioned backup of mcp_config and document any changes for reproducibility.