What is Mcp With Python

MCP_with_python is a RandomUser MCP Server built with FastAPI, designed to integrate MCP tools with a custom chatbot and a proxy for connecting to Claude Desktop. It utilizes the Model Context Protocol (MCP) to enhance AI capabilities by generating random user profiles.

Use cases

Use cases include generating random user profiles for testing applications, enhancing chatbot interactions with user data, and integrating with other AI systems for improved functionality.

How to use

To use MCP_with_python, clone the repository, install the required dependencies, and set up the environment. You can access the tools via HTTP requests to the MCP server endpoints, such as ‘/mcp/tools’ and ‘/mcp/call_tool’.

Key features

Key features include an HTTP-based MCP Server using FastAPI, a command-line chatbot interface, integration with Claude Desktop through a proxy, and extensibility for adding or modifying tools.

Where to use

MCP_with_python can be used in fields such as AI development, chatbot creation, user data generation, and any application requiring random user profile generation.

RandomUser MCP Server and Chatbot

This repository contains a RandomUser MCP Server built with FastAPI, a custom chatbot integrating MCP tools with Gemini-2.0-Flash, and a proxy to connect the server to Claude Desktop. The project leverages the Model Context Protocol (MCP) to extend AI capabilities with tools for generating random user profiles, demonstrating a practical application of MCP in both standalone and integrated environments.

This project uses GOOGLE GEMINI LLM, click there to get your free API keys: https://aistudio.google.com/

Overview

The RandomUser MCP Server fetches user data (name, country, age, etc.) from https://randomuser.me/api/ and exposes three tools:

• get_random_user: Fetch a single random user profile.
• get_multiple_users: Fetch multiple user profiles (1-10).
• get_user_by_gender: Fetch a user profile by gender (male/female).

The Chatbot uses these tools via HTTP requests or falls back to Gemini-2.0-Flash for general queries. The Proxy bridges the HTTP server to Claude Desktop’s stdio-based MCP interface, enabling tool usage within Claude.

Features

• MCP Server: HTTP-based server with FastAPI, exposing tools via /mcp/tools and /mcp/call_tool.
• Chatbot: Command-line interface blending MCP tools with Gemini-2.0-Flash responses.
• Claude Integration: Proxy script to connect the server to Claude Desktop.
• Extensible: Easy to add new tools or modify existing ones.

Prerequisites

• Python 3.8+
• Claude Desktop (optional, for MCP integration)
• Gemini API Key (for chatbot LLM functionality)

Installation

1. Clone the Repository:

   git clone https://github.com/shelwyn/MCP_with_python.git
   cd MCP_with_python
   

2. Install Dependencies:

   pip install -r requirements.txt
   

3. Set Up Environment:
   Create a .env file in the root directory:

   GEMINI_API_KEY=your_gemini_api_key_here
   

   Replace your_gemini_api_key_here with your actual Gemini API key from Google.

File Structure

randomuser-mcp/
├── mcp_server.py           # HTTP-based MCP server
├── chatbot.py              # Custom chatbot with MCP and Gemini
├── proxy.py                # Stdio-to-HTTP proxy for Claude Desktop
├── .env                    # Environment variables (not tracked)
├── requirements.txt        # Project dependencies
├── claude_desktop_config.json  # Claude Desktop MCP config (example)
└── README.md               # This file

Usage

1. Running the MCP Server

Start the HTTP server:

python mcp_server.py

• Runs on http://localhost:8000
• Endpoints: /mcp/sse, /mcp/tools, /mcp/call_tool

2. Running the Chatbot

Launch the chatbot:

python chatbot.py

Examples:

You: Get me a random user
Bot: Full Name: Mr John Doe
     Country: United States
     Age: 34
     ...

You: Tell me about the sun
Bot: The Sun is a star at the center of our solar system...

You: exit

3. Integrating with Claude Desktop

1. Start the MCP Server:

   python mcp_server.py
   

2. Configure Claude:
   Edit claude_desktop_config.json (e.g., C:\Users\<YourUsername>\AppData\Roaming\Claude\claude_desktop_config.json):

   {
     "mcpServers": {
       "randomuser_proxy": {
         "command": "python",
         "args": [
           "C:/path/to/proxy.py"
         ]
       }
     }
   }
   

   Update the path to proxy.py.

3. Restart Claude Desktop:

   • Close and reopen Claude Desktop.
   • Look for the hammer icon (🔨) in the input box.
   • Test with: “Use get_random_user” or “Call get_multiple_users with count 3”.

Requirements

requests==2.31.0
fastapi==0.103.2
uvicorn==0.23.2
sse-starlette==1.6.5
mcp[cli]==0.1.0
google-generativeai==0.4.0
python-dotenv==1.0.0

How It Works

MCP Server (mcp_server.py)

• Tools: Defined in RandomUserTool class, fetching data from randomuser.me.
• FastAPI: Hosts HTTP endpoints, with MCP logic from mcp.server.Server.
• Endpoints:
  • /mcp/tools: Lists tools in MCP format.
  • /mcp/call_tool: Executes tools with POSTed arguments.

Chatbot (chatbot.py)

• Tool Detection: Parses input for keywords (e.g., “random user”) to call MCP tools via HTTP.
• LLM Fallback: Uses Gemini-2.0-Flash for non-tool queries.
• HTTP Client: Communicates with http://localhost:8000.

Proxy (proxy.py)

• Bridge: Translates Claude’s stdio JSON-RPC requests to HTTP calls.
• Methods: Handles initialize, tools/list, tools/call, and returns empty responses for resources/list and prompts/list.
• Runs as Subprocess: Launched by Claude Desktop via config.

Troubleshooting

No Hammer Icon in Claude:

• Ensure mcp_server.py is running before starting Claude.
• Check logs in C:\Users\<YourUsername>\AppData\Roaming\Claude\Logs.

Chatbot Errors:

• Verify .env has a valid GEMINI_API_KEY.
• Ensure MCP server is running.

Proxy Issues:

• Confirm the path in claude_desktop_config.json is correct.
• Add print("Debug: ...", file=sys.stderr) in proxy.py for logs.

Contributing

1. Fork the repository.
2. Create a feature branch (git checkout -b feature/new-tool).
3. Commit changes (git commit -m "Add new tool").
4. Push to the branch (git push origin feature/new-tool).
5. Open a Pull Request.

License

This project is licensed under the MIT License - see the LICENSE file for details.

Acknowledgments

• Anthropic: For Claude Desktop and MCP support.
• Google: For Gemini-2.0-Flash API.
• RandomUser.me: For the free user data API.

Contact

For questions or suggestions, open an issue or reach out via GitHub.