MCP-ChatBot

A versatile chatbot application that uses MCP (Modular Capability Protocol) to interact with multiple service backends.

Overview

MCP-ChatBot is a containerized application that demonstrates the use of Modular Capability Protocol (MCP) to enable LLM interactions with external services. This implementation includes a weather service backend and a Streamlit-based frontend that allows users to query for weather information through natural language.

Features

- Containerized Architecture: Separate containers for the MCP server and client application
- Weather Service Integration: Real-time weather data using WeatherAPI
- Streamlit UI: Clean, responsive user interface for interacting with the chatbot
- Extensible Design: Ready to add more MCP servers for additional capabilities
- GPT-4o Integration: Powered by OpenAI's GPT-4o model for natural language understanding

Architecture

The application consists of two main components:

1. MCP Server: A FastMCP-based service that handles weather data retrieval
2. Streamlit Client: A web-based UI for interacting with the chatbot

Getting Started

Prerequisites

- Docker and Docker Compose
- WeatherAPI Key (sign up at WeatherAPI)
- OpenAI API Key (sign up at OpenAI)

Installation

1. Clone the repository:

git clone https://github.com/yourusername/MCP-ChatBot.git
cd MCP-ChatBot

2. Create an .env file based on the example:

cp .env.example .env

3. Edit the .env file and add your API keys:

PYTHONUNBUFFERED=1
OPENAI_API_KEY=your_openai_api_key_here
WEATHER_API_KEY=your_weather_api_key_here

4. Launch the application using Docker Compose:

docker-compose up --build

5. Access the Streamlit UI at: http://localhost:8501

Usage

Once the application is running, you can interact with the chatbot through the Streamlit interface:

1. Type natural language queries about weather in the text input
2. Examples:
- "What's the weather like in New York?"
- "How hot is it in Tokyo right now?"
- "Tell me about the weather in London"

Extending the Application

Adding New MCP Servers

1. Create a new server file in the servers directory
2. Add the new service to the docker-compose.yml file
3. Update the client.py to include the new server in the agent configuration

Example of adding a new server in client.py:

server_1 = MCPServerHTTP(url="http://mcp_server:8001/sse")
server_2 = MCPServerHTTP(url="http://new_server:8002/sse")  # New server
return Agent("openai:gpt-4o", mcp_servers=[server_1, server_2])

Development

Project Structure

MCP-ChatBot/
├── .env.example             # Example environment variables
├── .gitignore               # Git ignore file
├── client.py                # Streamlit client application
├── docker-compose.yml       # Docker Compose configuration
├── Dockerfile.client        # Dockerfile for Streamlit client
├── Dockerfile.server        # Dockerfile for MCP server
├── LICENSE                  # MIT license
├── README.md                # Project documentation
└── servers/                 # MCP server implementations
    └── server.py            # Weather service implementation

Technology Stack

- FastMCP: Framework for creating MCP servers
- Streamlit: Web framework for the UI
- Pydantic-AI: Agent system for LLM interactions
- Docker: Containerization platform
- OpenAI GPT-4o: LLM for natural language processing

Troubleshooting

Common Issues

1. Connection Failed: Ensure that all services are up and running. The client has a retry mechanism, but if it fails, restart the application.

2. API Key Errors: Verify that you've added valid API keys to the .env file.

3. Docker Network Issues: If containers can't communicate, check the Docker network configuration and ensure the service names match in the code.

License

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

Acknowledgments

- FastMCP for the MCP server implementation
- Streamlit for the frontend framework
- WeatherAPI for weather data

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Created by Murali Anand © 2025