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:

   git clone https://github.com/3choff/mcp-chatbot.git
   cd mcp-chatbot

2. Install the dependencies:

   pip install -r requirements.txt

3. Set up environment variables:

   Create a .env file in the root directory and add your API key:

   LLM_API_KEY=your_api_key_here
   

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:

   {
   "mcpServers": {
   "sqlite": {
   "command": "uvx",
   "args": ["mcp-server-sqlite", "--db-path", "./test.db"]
   },
   "puppeteer": {
   "command": "npx",
   "args": ["-y", "@modelcontextprotocol/server-puppeteer"]
   }
   }
   }

   Environment variables are supported as well. Pass them as you would with the Claude Desktop App.

   Example:

   {
   "mcpServers": {
   "server_name": {
   "command": "uvx",
   "args": ["mcp-server-name", "--additional-args"],
   "env": {
   "API_KEY": "your_api_key_here"
   }
   }
   }
   }

Usage

1. Run the client:

   python main.py

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

flowchart TD
A[Start] --> B[Load Configuration]
B --> C[Initialize Servers]
C --> D[Discover Tools]
D --> E[Format Tools for LLM]
E --> F[Wait for User Input]
F --> G{User Input}
G --> H[Send Input to LLM]
H --> I{LLM Decision}
I -->|Tool Call| J[Execute Tool]
I -->|Direct Response| K[Return Response to User]
J --> L[Return Tool Result]
L --> M[Send Result to LLM]
M --> N[LLM Interprets Result]
N --> O[Present Final Response to User]
K --> O
O --> F

Loading

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
   • LLM response is parsed:
     • If it's a tool call → execute tool and return result
     • If it's a direct response → return to user
   • Tool results are sent back to LLM for interpretation
   • Final response is presented to user

3. Tool Integration:

   • Tools are dynamically discovered from MCP servers
   • Tool descriptions are automatically included in system prompt
   • Tool execution is handled through standardized MCP protocol

Contributing

Feedback and contributions are welcome. If you encounter any issues or have suggestions for improvements, please create a new issue on the GitHub repository.

If you'd like to contribute to the development of the project, feel free to submit a pull request with your changes.

License

This project is licensed under the MIT License.