3choff/mcp-chatbot

This chatbot example demonstrates how to integrate the Model Context Protocol (MCP) into a simple CLI chatbot. The implementation showcases MCP's flexibility by supporting multiple tools through MCP servers and is compatible with any LLM provider that follows OpenAI API standards.
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Key Features
- **LLM Provider Flexibility**: Works with any LLM that follows OpenAI API standards (tested with Llama 3.2 90b on Groq and GPT-4o mini on GitHub Marketplace).
- **Dynamic Tool Integration**: Tools are declared in the system prompt, ensuring maximum compatibility across different LLMs.
- **Server Configuration**: Supports multiple MCP servers through a simple JSON configuration file like the Claude Desktop App.
Requirements
- Python 3.10
- python-dotenv
- requests
- mcp
- uvicorn
Installation
1. **Clone the repository:**
2. **Install the dependencies:**
3. **Set up environment variables:**
Create a .env file in the root directory and add your API key:
4. **Configure servers:**
The servers_config.json follows the same structure as Claude Desktop, allowing for easy integration of multiple servers.
Here's an example:
Environment variables are supported as well. Pass them as you would with the Claude Desktop App.
Example:
Usage
1. **Run the client:**
2. **Interact with the assistant:**
The assistant will automatically detect available tools and can respond to queries based on the tools provided by the configured servers.
3. **Exit the session:**
Type quit or exit to end the session.
Architecture
- **Tool Discovery**: Tools are automatically discovered from configured servers.
- **System Prompt**: Tools are dynamically included in the system prompt, allowing the LLM to understand available capabilities.
- **Server Integration**: Supports any MCP-compatible server, tested with various server implementations including Uvicorn and Node.js.
Class Structure
- **Configuration**: Manages environment variables and server configurations
- **Server**: Handles MCP server initialization, tool discovery, and execution
- **Tool**: Represents individual tools with their properties and formatting
- **LLMClient**: Manages communication with the LLM provider
- **ChatSession**: Orchestrates the interaction between user, LLM, and tools
Logic Flow
1. **Initialization**:
- Configuration loads environment variables and server settings
- Servers are initialized with their respective tools
- Tools are discovered and formatted for LLM understanding
2. **Runtime Flow**:
- User input is received
- Input is sent to LLM with context of available tools