What is Model Context Protocol Server

The Model Context Protocol (MCP) Server is an implementation designed to manage context between AI models and applications. It standardizes the process of creating sessions, storing context data, processing this data with AI models, merging contexts, and querying models with context-aware prompts.

Use cases

Use cases for the MCP Server include creating interactive AI assistants, managing user sessions in applications, summarizing user interactions, and enhancing AI model responses based on user context.

How to use

To use the MCP Server, clone the repository, install the necessary dependencies using ‘npm install’, configure environment variables in the ‘.env’ file, and start the server with ‘npm start’. API endpoints are available for creating and managing sessions and contexts, as well as querying models.

Key features

Key features of the MCP Server include session management, context storage and retrieval, context processing with AI models, context merging and summarization, and model querying with context.

Where to use

The MCP Server can be utilized in various fields such as AI development, chatbot applications, personalized user experiences, and any application requiring context management between users and AI models.

Model Context Protocol (MCP) Server

A server implementation of the Model Context Protocol for managing context between AI models and applications.

What is MCP?

The Model Context Protocol (MCP) is a protocol for managing context between AI models and applications. It provides a standardized way to:

1. Create and manage sessions for user interactions
2. Store and retrieve context data
3. Process context with AI models
4. Merge and summarize contexts
5. Query models with context-aware prompts

Features

• Session management
• Context storage and retrieval
• Context processing with AI models
• Context merging and summarization
• Model querying with context

Getting Started

1. Clone this repository
2. Install dependencies:

   npm install
   

3. Configure environment variables in .env:

   PORT=3000
   API_KEY=your_api_key_here
   MODEL_ENDPOINT=http://localhost:8000/v1/completions
   CONTEXT_DB_PATH=./data/context_db.json
   MODEL_TYPE=mistral
   

4. Start the server:

   npm start
   

API Endpoints

Sessions

• POST /api/session - Create a new session
• GET /api/session/:sessionId - Get a session by ID
• PATCH /api/session/:sessionId - Update a session
• DELETE /api/session/:sessionId - Delete a session
• GET /api/session/:sessionId/contexts - Get all contexts for a session

Contexts

• POST /api/context - Create a new context
• GET /api/context/:contextId - Get a context by ID
• PATCH /api/context/:contextId - Update a context
• DELETE /api/context/:contextId - Delete a context
• POST /api/context/merge - Merge multiple contexts
• POST /api/context/:contextId/summarize - Summarize a context

Models

• POST /api/model/query - Query the model with a prompt and context
• POST /api/model/process - Process context with a model

Authentication

All API endpoints are protected with API key authentication. Include your API key in the Authorization header:

Authorization: Bearer your_api_key_here

Data Storage

By default, context data is stored in a JSON file at ./data/context_db.json. You can configure a different path in the .env file.

Model Integration

The server is designed to work with any AI model that supports a compatible API. Configure the model endpoint in the .env file.

Supported Model Types

The server supports different model types through the MODEL_TYPE environment variable:

• mistral - For Mistral AI models
• openai - For OpenAI models
• anthropic - For Anthropic models
• generic - For other model providers

SSL Certificate Verification

For development purposes, SSL certificate verification is disabled by default. In production environments, you should:

1. Either provide proper certificates
2. Or remove the rejectUnauthorized: false option in modelManager.js

Example Usage

Create a Session

curl -X POST http://localhost:3000/api/session \
  -H "Authorization: Bearer your_api_key_here" \
  -H "Content-Type: application/json" \
  -d '{"metadata": {"user": "example_user"}}'

Create a Context

curl -X POST http://localhost:3000/api/context \
  -H "Authorization: Bearer your_api_key_here" \
  -H "Content-Type: application/json" \
  -d '{
    "sessionId": "session_123",
    "data": {
      "conversation": [
        {"role": "user", "content": "Hello, how are you?"},
        {"role": "assistant", "content": "I'm doing well, thank you for asking!"}
      ]
    }
  }'

Query a Model with Context

curl -X POST http://localhost:3000/api/model/query \
  -H "Authorization: Bearer your_api_key_here" \
  -H "Content-Type: application/json" \
  -d '{
    "prompt": "What was my last question?",
    "contextId": "ctx_123",
    "sessionId": "session_123"
  }'

License

MIT