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

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.