About MCP Server
This is a modular MCP server implementation in Python for AI models to discover and invoke tools through a standard protocol. Its separate tool modules suit developers extending a server with additional tools.
Key features
- 01Discover tools through a standard protocol
- 02Invoke registered tools
- 03Organize tools as Python modules
- 04Add tools with a decorator
Use cases
- 01Build an extensible MCP server
- 02Keep tools in separate modules
- 03Expose tool calls to AI models
MCP support
The server implements MCP so AI models can discover and invoke its tools.
Configuration
Local server config is the same for every client. Copy it into your client’s MCP config file.
We haven’t collected a package or remote endpoint for this server. Follow the instructions in the project README.
Tools
Local servers expose tools only once running on your machine, and the maintainer has not provided a tool list.
Details
From the project READMEMCP Server
A scalable and modular Model Context Protocol (MCP) server implementation using Python.
Overview
This project implements an MCP-compliant server that allows AI models to discover and invoke tools through a standardized protocol. The server is designed to be modular, with each tool implemented as a separate Python module.
Installation
``` pip install -r requirements.txt ```
Usage
To run the server:
``` python -m src.server ```
Adding New Tools
Create a new Python file in the `src/tools` directory with functions using the `@tool` decorator.
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.