Which integrations are available for this server?

Provides access to OpenAI's language models including GPT-3.5-turbo, GPT-4, and GPT-4o for chat completions and AI-powered conversations. Implements persistent chat memory storage using SQLite database, allowing conversation history and metadata to be stored and retrieved across sessions.

How do I use MCP Server with LLM Integration?

1. Click on "Install Server". 2. Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state. 3. In the chat, type @ followed by the MCP server name and your instructions, e.g., "@MCP Server with LLM Integration ask Claude to summarize our conversation about project requirements" That's it! The server will respond to your query, and you can continue using it as needed. Here is a step-by-step guide with screenshots.

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

Clone this repository:

git clone <repository-url> cd MCP

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

Fork the repository
Create a feature branch
Make your changes
Add tests for new functionality
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()"

This server cannot be installed

-

security - not tested

F

license - not found

-

quality - not tested

How are these scores calculated?

Resources

GitHub Repository

Need Help?

Report Issue

Related Servers

MCP Server with LLM Integration