https://github.com/muralianand12345/MCP-ChatBot
Simple MCP Client-Server example
https://github.com/muralianand12345/MCP-ChatBot
chatbot fastmcp llm mcp-client mcp-server pydantic-ai
Last synced: about 2 months ago
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Simple MCP Client-Server example
- Host: GitHub
- URL: https://github.com/muralianand12345/MCP-ChatBot
- Owner: muralianand12345
- License: mit
- Created: 2025-04-08T09:40:00.000Z (7 months ago)
- Default Branch: main
- Last Pushed: 2025-04-08T09:44:58.000Z (7 months ago)
- Last Synced: 2025-04-08T10:34:49.478Z (7 months ago)
- Topics: chatbot, fastmcp, llm, mcp-client, mcp-server, pydantic-ai
- Language: Python
- Homepage:
- Size: 0 Bytes
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
- awesome-mcp-servers - **MCP-ChatBot** - Simple MCP Client-Server example `python` `chatbot` `fastmcp` `llm` `mcp-client` `pip install git+https://github.com/muralianand12345/MCP-ChatBot` (AI/ML)
- awesome-mcp-servers - **MCP-ChatBot** - Simple MCP Client-Server example `python` `chatbot` `fastmcp` `llm` `mcp-client` `pip install git+https://github.com/muralianand12345/MCP-ChatBot` (🤖 AI/ML)
README
# 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](https://www.weatherapi.com/))
- OpenAI API Key (sign up at [OpenAI](https://platform.openai.com/))
### Installation
1. Clone the repository:
```bash
git clone https://github.com/yourusername/MCP-ChatBot.git
cd MCP-ChatBot
```
2. Create an `.env` file based on the example:
```bash
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:
```bash
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:
```python
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](LICENSE) file for details.
## Acknowledgments
- [FastMCP](https://github.com/Significant-Gravitas/fastmcp) for the MCP server implementation
- [Streamlit](https://streamlit.io/) for the frontend framework
- [WeatherAPI](https://www.weatherapi.com/) for weather data
---
Created by [Murali Anand](https://github.com/yourusername) © 2025