MCP Local Project Memory

Local Project Memory MCP Server

A Model Context Protocol (MCP) server for storing and retrieving project-specific context and memories for LLMs. This server enables AI assistants to maintain persistent knowledge about your project across sessions.

Features

• Store Memories: Save project context as markdown files with metadata
• Retrieve Memories: Access stored information by filename
• Search Memories: Find relevant context using keyword search
• List Memories: Browse all stored memories with optional tag filtering
• Update Memories: Modify existing memories while preserving metadata
• Delete Memories: Remove outdated or irrelevant memories
• Memory Instructions: Get guidance on how to use the memory system

Installation

From Source

cd local-project-memory
pip install -e .

Development Installation

pip install -e ".[dev]"

Configuration

The server stores memories in .ai/context/memories/ within your project directory. Each memory is saved as a markdown file with frontmatter containing metadata:

---
title: Memory Title
created: 2025-01-15T10:30:00
tags: tag1, tag2
---

Memory content goes here...

Usage

Running the Server

local-project-memory

Or with Python:

python -m local_project_memory

Testing with MCP Inspector

npx @modelcontextprotocol/inspector local-project-memory

Adding to Claude Desktop

Edit your Claude Desktop config file:

• macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
• Windows: %APPDATA%\Claude\claude_desktop_config.json

Add the server configuration:

{
  "mcpServers": {
    "local-project-memory": {
      "command": "local-project-memory"
    }
  }
}

Or if installed with uvx:

{
  "mcpServers": {
    "local-project-memory": {
      "command": "uvx",
      "args": ["local-project-memory"]
    }
  }
}

Restart Claude Desktop to load the server.

Available Tools

store_memory

Store a new memory with title, content, and optional tags.

Parameters:

• title (string): Title/name for the memory
• content (string): Content to store
• tags (string, optional): Comma-separated tags

retrieve_memory

Retrieve a memory by filename.

Parameters:

• filename (string): Filename of the memory to retrieve

list_memories

List all stored memories with optional tag filtering.

Parameters:

• tag_filter (string, optional): Filter memories by tag

search_memories

Search through all memories for a query string.

Parameters:

• query (string): Search query

update_memory

Update an existing memory's content.

Parameters:

• filename (string): Filename of the memory to update
• content (string): New content

delete_memory

Delete a memory file.

Parameters:

• filename (string): Filename of the memory to delete

get_memory_instructions

Get instructions on how to interact with the memory system.

Development

Running Tests

pytest

Code Formatting

black src tests
ruff check src tests

Type Checking

mypy src

Project Structure

local-project-memory/
├── src/
│   └── local_project_memory/
│       ├── __init__.py
│       ├── __main__.py
│       ├── server.py          # Main server setup
│       ├── config.py          # Configuration management
│       └── tools/
│           └── __init__.py    # Memory management tools
├── tests/
│   ├── __init__.py
│   ├── conftest.py           # Test fixtures
│   └── test_tools.py         # Tool tests
├── pyproject.toml            # Project metadata and dependencies
├── pytest.ini                # Pytest configuration
└── README.md                 # This file

Memory Storage

Memories are stored locally in your project at:

.ai/context/memories/

Each memory file follows the naming convention:

YYYYMMDD-HHMMSS-memory-title.md

Instructions for the memory system are stored at:

.ai/context/memory-instructions.md

Use Cases

• Project Context: Store architectural decisions, patterns, and conventions
• Code Knowledge: Document complex implementations and their rationale
• API Documentation: Keep track of external APIs and their usage
• Best Practices: Record project-specific guidelines and standards
• Issue Tracking: Maintain notes about known issues and their solutions
• Session Continuity: Share context between different AI assistant sessions

License

MIT