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MCP Server Implementation

@yisu201506

Repository

Basic information

Category

Other

Transports

stdio

Author

yisu201506

About MCP Server Implementation

Repository for MCP server implementation

#mcp-server#mcp#flask#llm#api

MCP Server Implementation config

Add this server to your MCP-compatible client using the configuration below.

{
  "mcpServers": {
    "mcp-server-yisu201506": {
      "command": "python",
      "args": [
        "-m",
        "venv",
        "venv"
      ]
    }
  }
}

MCP Server Implementation tools

Tool list not available yet

This listing does not currently have a tool list to display. This does not mean the server has no tools; check its documentation for supported capabilities.

We auto-extract tools from the README. The maintainer can list them under a ## Tools heading to populate this section.

Overview

What is MCP Server Implementation?

A Flask-based implementation of the Model Context Protocol (MCP) that extends Large Language Models with external tools like weather and calculator, using text-based tool invocation directly in the context window.

How to use MCP Server Implementation?

Clone the repository, create a Python virtual environment, install dependencies, set environment variables for API keys, then run flask run or gunicorn app:app. Send chat messages via the POST /chat endpoint with a JSON body containing a messages array.

Key features of MCP Server Implementation

  • Complete MCP parsing, execution, and response handling
  • Sample weather and calculator tools with parameter validation
  • Maintains conversation context across multiple interactions
  • Regex‑based parsing for flexible tool invocation detection
  • Flask REST API for easy chat integration

Use cases of MCP Server Implementation

  • Adding real‑time weather data to an LLM‑powered chatbot
  • Enabling a language model to perform mathematical calculations
  • Building custom tool integrations by inheriting from the Tool base class
  • Demonstrating MCP workflow for educational or prototyping purposes

Frequently asked questions

1

What is MCP and how does it differ from function calling?

MCP places tool definitions in the prompt text and parses natural‑language responses for tool calls, while function calling uses structured JSON in API parameters and requires API support.

2

What are the runtime dependencies?

Python 3, Flask, Gunicorn (optional for production), and API keys for the LLM and weather service stored in environment variables.

3

Where is conversation state stored?

The server maintains conversation history across multiple interactions by adding processed responses and tool results back into the message history.

4

What transport does the server use?

HTTP via the Flask development server (default port 5000) or Gunicorn in production.

5

How are tools authenticated?

Weather tool requires a `WEATHER_API_KEY` environment variable; LLM requests require an `LLM_API_KEY`. Both are set in a `.env` file.

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