MCP Server

A Model Context Protocol (MCP) server implementation with LLM integration and chat memory capabilities.

Features

• MCP Server: Full Model Context Protocol server implementation

• LLM Integration: Support for OpenAI and Anthropic models

• Chat Memory: Persistent conversation storage and retrieval

• Tool System: Extensible tool framework for various operations

Installation

1. Clone this repository:

git clone <repository-url>
cd MCP

2. Install dependencies:

pip install -r requirements.txt

Or using the development environment:

pip install -e .[dev]

Configuration

Set up your API keys as environment variables:

export OPENAI_API_KEY="your-openai-api-key"
export ANTHROPIC_API_KEY="your-anthropic-api-key"

Or create a .env file:

OPENAI_API_KEY=your-openai-api-key
ANTHROPIC_API_KEY=your-anthropic-api-key

Usage

Running the MCP Server

Start the server using the command line:

python -m mcp

Or run directly:

python mcp.py

Available Tools

The server provides the following tools:

Echo Tool

Simple echo functionality for testing.

{
  "name": "echo",
  "arguments": {
    "text": "Hello, world!"
  }
}

Chat Memory Tools

Store Memory

{
  "name": "store_memory",
  "arguments": {
    "conversation_id": "conv_123",
    "content": "User preferences: dark mode enabled",
    "metadata": {"type": "preference"}
  }
}

Get Memory

{
  "name": "get_memory",
  "arguments": {
    "conversation_id": "conv_123"
  }
}

LLM Chat Tool

{
  "name": "llm_chat",
  "arguments": {
    "message": "What is the capital of France?",
    "model": "gpt-3.5-turbo"
  }
}

Supported Models

OpenAI Models:

• gpt-3.5-turbo

• gpt-4

• gpt-4-turbo

• gpt-4o

Anthropic Models:

• claude-3-haiku-20240307

• claude-3-sonnet-20240229

• claude-3-opus-20240229

Development

Running Tests

pytest

Code Formatting

black .
isort .

Type Checking

mypy .

Architecture

Components

• mcp.py: Main MCP server implementation and tool registration

• llmintegrationsystem.py: LLM provider integration and chat completions

• chatmemorysystem.py: Persistent conversation storage with SQLite

Database Schema

The chat memory system uses SQLite with two main tables:

• memories: Individual conversation messages and metadata

• conversation_summaries: Conversation overviews and statistics

Contributing

1. Fork the repository

2. Create a feature branch

3. Make your changes

4. Add tests for new functionality

5. Submit a pull request

License

MIT License - see LICENSE file for details.

Troubleshooting

Common Issues

API Key Errors Ensure your API keys are properly set in environment variables.

Database Permissions The server creates a chat_memory.db file in the current directory. Ensure write permissions.

Port Conflicts The MCP server uses stdio communication by default. No port configuration needed.

Logging

Enable debug logging:

PYTHONPATH=. python -c "import logging; logging.basicConfig(level=logging.DEBUG); import mcp; mcp.main()"