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MCP Server

by yisu201506

AI/Development ToolsFlaskTool InvocationAPI

A Flask-based implementation of the Model Context Protocol (MCP) for enhancing Large Language Model capabilities with external tools. It allows LLMs to invoke tools directly from their text output.

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What is MCP Server?

This is a Flask server that implements the Model Context Protocol (MCP). MCP extends LLM capabilities by allowing them to use external tools through natural language tool invocation within the model's text output.

How to use MCP Server?

  1. Clone the repository. 2. Set up a virtual environment and install dependencies. 3. Configure environment variables (LLM_API_KEY, WEATHER_API_KEY). 4. Run the Flask server using flask run or gunicorn app:app. 5. Use the /chat API endpoint to send chat messages.

Key features of MCP Server

  • Complete MCP Implementation

  • Sample Weather and Calculator Tools

  • Conversation Flow Management

  • Regex-Based Parsing

  • Flask API

Use cases of MCP Server

  • Extending LLM capabilities with external services

  • Integrating LLMs with real-world data

  • Enabling LLMs to perform calculations

  • Creating conversational agents that can use tools

FAQ from MCP Server

What is MCP?

Model Context Protocol (MCP) is a method for extending LLM capabilities through tool invocation directly in the model's text output.

How does MCP differ from function calling?

MCP defines tools in the prompt text, uses natural language invocation, and relies on text parsing, while function calling defines tools in API parameters, uses structured JSON, and requires API integration.

How do I add my own tools?

Create a new class inheriting from Tool, define parameters and execution logic, and register it with the MCP handler.

What is the purpose of the mcp_example.py file?

The mcp_example.py file provides a standalone example of how to use the MCP implementation.

What are the key dependencies for this project?

The key dependencies are listed in the requirements.txt file and include Flask and other libraries for handling API requests, parsing, and tool execution.