MCP Memory Tracker

A Model Context Protocol (MCP) server that provides persistent memory capabilities using OpenAI's vector stores. This allows AI assistants to save and search through memories across conversations.

Features

Save Memories: Store text-based memories in OpenAI vector stores
Search Memories: Semantic search through saved memories using natural language queries
Persistent Storage: Memories are stored in OpenAI's cloud infrastructure
MCP Compatible: Works with any MCP-compatible client (like Claude Desktop)

Prerequisites

Python 3.8+
OpenAI API key
UV package manager (recommended) or pip

Installation

Clone the repository:

git clone <repository-url>
cd mcp-memory-tracker

Install dependencies:

# Using UV (recommended)
uv sync

# Or using pip
pip install -r requirements.txt

Set up environment variables: Create a .env file in the project root:

OPENAI_API_KEY=your_openai_api_key_here

Usage

Running the MCP Server

# Using UV
uv run server.py

# Or using Python directly
python server.py

Available Tools

`save_memory(memory: str)`

Saves a text memory to the vector store.

Parameters:

memory (string): The text content to save

Returns:

{
  "status": "saved",
  "vector store id": "vs_xxxxx"
}

Example:

save_memory("I met John at the coffee shop on Main Street. He's a software engineer who loves hiking.")

`search_memories(query: str)`

Searches through saved memories using semantic search.

Parameters:

query (string): Natural language search query

Returns:

{
  "status": "success",
  "results": ["matching memory content..."]
}

Example:

search_memories("Who did I meet at the coffee shop?")

Integration with MCP Clients

Claude Desktop

Add this server to your Claude Desktop configuration:

{
  "mcpServers": {
    "memory-tracker": {
      "command": "uv",
      "args": ["run", "/path/to/mcp-memory-tracker/server.py"],
      "env": {
        "OPENAI_API_KEY": "your_api_key_here"
      }
    }
  }
}

Other MCP Clients

This server implements the standard MCP protocol and should work with any compatible client. Refer to your client's documentation for configuration details.

How It Works

Vector Store Management: The server automatically creates and manages an OpenAI vector store named "memories"
Memory Storage: When you save a memory, it's uploaded as a text file to the vector store
Semantic Search: The search functionality uses OpenAI's vector search capabilities to find relevant memories based on meaning, not just keywords

Configuration

The server uses the following constants that can be modified in server.py:

VECTOR_STORE_NAME: Name of the OpenAI vector store (default: "memories")

Dependencies

fastmcp: MCP server framework
openai: OpenAI Python SDK
python-dotenv: Environment variable management

Troubleshooting

Common Issues

"OPENAI_API_KEY not found": Make sure your .env file is properly configured
"'SyncPage' object has no attribute...": This indicates an API response structure issue - check your OpenAI SDK version
File upload errors: Ensure your OpenAI API key has vector store permissions

Debug Mode

Add print statements to see detailed responses:

print(f"Vector store ID: {vector_store.id}")
print(f"Search results: {results}")

Contributing

Fork the repository
Create a feature branch
Make your changes
Submit a pull request

License

[Add your license here]

Support

For issues and questions:

Check the troubleshooting section
Review OpenAI API documentation
Check MCP protocol documentation

This server cannot be installed

security - not tested

license - not found

quality - not tested

How are these scores calculated?

A Model Context Protocol (MCP) server that provides persistent memory capabilities using OpenAI's vector stores, allowing AI assistants to save and search through memories across conversations.

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