UtkarshSinghShorthillsAI/LLM_MCP_SERVER

LLM-MCP-Server

A Model Context Protocol (MCP) server that provides LLM chatting capabilities as tools with robust conversation history management. This server supports Azure OpenAI and Google Gemini providers.

Features

• Conversation History Management: Maintains robust conversation context across multiple exchanges
• Multiple LLM Providers: Support for Azure OpenAI and Google Gemini
• Session Management: Create, manage, and clear conversation sessions
• Tool Integration: Seamless integration with MCP clients
• Error Handling: Comprehensive error handling and logging
• Provider Testing: Built-in connectivity testing for LLM providers

Installation

1. Clone this repository:

git clone <repository-url>
cd LLM_MCP_SERVER

2. Install dependencies:

npm install

3. Set up environment variables:

cp .env.example .env

4. Edit .env file with your API keys and configuration

5. Build the project:

npm run build

Configuration

Create a .env file in the root directory with the following variables:

Azure OpenAI Configuration

AZURE_OPENAI_API_KEY=your_azure_openai_api_key
AZURE_OPENAI_ENDPOINT=https://your-resource.openai.azure.com/
AZURE_OPENAI_DEPLOYMENT_NAME=gpt-4

Gemini Configuration

GEMINI_API_KEY=your_gemini_api_key

Server Configuration

DEFAULT_LLM_PROVIDER=azure-openai  # or 'gemini'
MAX_CONVERSATION_HISTORY=20        # Maximum messages to keep in history

Usage

Starting the Server

For development:

npm run dev

For production:

npm start

MCP Tools

The server provides the following tools:

1. chat

Chat with an LLM with conversation history support.

Parameters:

• message (required): The message to send to the LLM
• sessionId (optional): Session ID to maintain conversation history
• provider (optional): LLM provider to use ('azure-openai' or 'gemini')
• systemPrompt (optional): System prompt to set conversation context

Example:

{
  "message": "Hello, how are you?",
  "sessionId": "my-session",
  "provider": "azure-openai",
  "systemPrompt": "You are a helpful assistant."
}

2. clear_session

Clear a conversation session and its history.

Parameters:

• sessionId (required): The session ID to clear

3. list_sessions

List all active conversation sessions.

Parameters: None

4. test_provider

Test connectivity to a specific LLM provider.

Parameters:

• provider (required): LLM provider to test

Architecture

src/
├── index.ts                    # Main MCP server entry point
├── types.ts                    # TypeScript type definitions
├── config.ts                   # Configuration management
├── conversation-manager.ts     # Conversation history management
├── llm-service.ts             # Main LLM service orchestrator
└── providers/
    ├── azure-openai-provider.ts # Azure OpenAI implementation
    └── gemini-provider.ts       # Google Gemini implementation

Key Components

• LLMMCPServer: Main MCP server class that handles protocol communication
• LLMService: Orchestrates LLM providers and conversation management
• ConversationManager: Manages conversation sessions and history
• Providers: Individual LLM provider implementations

Conversation History Management

The server maintains robust conversation history with the following features:

• Session-based: Each conversation is isolated by session ID
• Automatic Trimming: Conversations are trimmed to maintain performance
• System Message Preservation: System messages are preserved during trimming
• Timestamp Tracking: All messages include timestamps
• Memory Management: Old conversations can be cleaned up automatically

Error Handling

The server includes comprehensive error handling:

• Provider-specific error messages
• Connection testing capabilities
• Graceful fallbacks
• Detailed logging

Development

Scripts

• npm run build: Build the TypeScript project
• npm run dev: Run in development mode with auto-reload
• npm start: Start the production server
• npm run clean: Clean build artifacts

Project Structure

The project follows a modular architecture with clear separation of concerns:

• Configuration management is centralized
• Each LLM provider is isolated
• Conversation management is provider-agnostic
• MCP protocol handling is separated from business logic

Contributing

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Add tests if applicable
5. Submit a pull request

License

MIT

Support

For issues and support, please check the documentation or create an issue in the repository.