README.MD•3.49 kB
# MCP Simple Chatbot
This 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.
## Requirements
- Python 3.10
- `python-dotenv`
- `requests`
- `mcp`
- `uvicorn`
## Installation
1. **Install the dependencies:**
```bash
pip install -r requirements.txt
```
2. **Set up environment variables:**
Create a `.env` file in the root directory and add your API key:
```plaintext
LLM_API_KEY=your_api_key_here
```
**Note:** The current implementation is configured to use the Groq API endpoint (`https://api.groq.com/openai/v1/chat/completions`) with the `llama-3.2-90b-vision-preview` model. If you plan to use a different LLM provider, you'll need to modify the `LLMClient` class in `main.py` to use the appropriate endpoint URL and model parameters.
3. **Configure servers:**
The `servers_config.json` follows the same structure as Claude Desktop, allowing for easy integration of multiple servers.
Here's an example:
```json
{
"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:
```json
{
"mcpServers": {
"server_name": {
"command": "uvx",
"args": ["mcp-server-name", "--additional-args"],
"env": {
"API_KEY": "your_api_key_here"
}
}
}
}
```
## Usage
1. **Run the client:**
```bash
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
1. **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
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