Related MCP Server Resources

Explore more AI models, providers, and integration options:

  • Explore AI Models
  • Explore AI Providers
  • Explore MCP Servers
  • LangDB Pricing
  • Documentation
  • AI Industry Blog
  • TxtAi Memory Vector Server
  • Powertools MCP Search Server
  • sanderkooger-mcp-server-ragdocs
  • mem0 MCP Server
  • Gemini MCP Image Generation Server
Back to MCP Servers
Memory MCP Server

Memory MCP Server

Public
tomschell/mcp-long-term-memory

A long-term memory storage system for LLMs that helps them remember context across multiple sessions using semantic search with embeddings to provide relevant historical information from past interactions and development decisions.

Verified
typescript
0 tools
May 29, 2025
Updated May 30, 2025

Memory MCP Server

A long-term memory storage system for LLMs using the Model Context Protocol (MCP) standard. This system helps LLMs remember the context of work done over the entire history of a project, even across multiple sessions. It uses semantic search with embeddings to provide relevant context from past interactions and development decisions.

Features

  • Project-based memory organization
  • Semantic search using Ollama embeddings (nomic-embed-text model, 768 dimensions)
  • Multiple memory types:
    • Conversations: Dialog context and important discussions
    • Code: Implementation details and changes
    • Decisions: Key architectural and design choices
    • References: Links to external resources and documentation
  • Rich metadata storage including:
    • Implementation status
    • Key decisions
    • Files created/modified
    • Code changes
    • Dependencies added
  • Tagging system for memory organization
  • Relationship tracking between memories

Prerequisites

  • Node.js (v18 or later)
  • Ollama running locally (for embeddings)
    • Must have the nomic-embed-text model installed
  • SQLite3

Installation

  1. Clone the repository
  2. Install dependencies:
    npm install
  3. Build the project:
    npm run build
  4. Create a .env file with required configuration:
    OLLAMA_HOST=http://localhost:11434
    DB_PATH=memory.db
    

Usage

  1. Start the server in development mode:

    npm run dev

    This will:

    • Compile TypeScript
    • Copy schema files
    • Start the server with auto-reload
  2. The server connects via stdio for Cursor compatibility

Database Schema

The system uses SQLite with the following tables:

Core Tables

  • projects: Project information and metadata
  • memories: Memory entries storing various types of development context
  • embeddings: Vector embeddings (768d) for semantic search capabilities

Organization Tables

  • tags: Memory organization tags
  • memory_tags: Many-to-many relationships between memories and tags
  • memory_relationships: Directed relationships between memory entries

MCP Tools

The following tools are available through the MCP protocol:

Memory Management

  • store-dev-memory: Create new development memories with:
    • Content
    • Type (conversation/code/decision/reference)
    • Tags
    • Code changes
    • Files created/modified
    • Key decisions
    • Implementation status
  • list-dev-memories: List existing memories with optional tag filtering
  • get-dev-memory: Retrieve specific memory by ID
  • search: Semantic search across memories using embeddings

Development

For development:

npm run dev

This will:

  1. Kill any existing server instances
  2. Rebuild the TypeScript code
  3. Copy the schema.sql to the dist directory
  4. Start the server in development mode

Dependencies

Key dependencies:

  • @modelcontextprotocol/sdk@^1.7.0: MCP protocol implementation
  • better-sqlite3@^9.4.3: SQLite database interface
  • node-fetch@^3.3.2: HTTP client for Ollama API
  • zod@^3.22.4: Runtime type checking and validation

Project Structure

memory-mcp-server/
├── src/
│   ├── db/
│   │   ├── init.ts     # Database initialization
│   │   └── service.ts  # Database service layer
│   ├── dev-memory.ts   # Development memory helpers
│   ├── index.ts        # Main server implementation
│   └── schema.sql      # Database schema
├── dist/               # Compiled JavaScript
├── package.json        # Project configuration
└── tsconfig.json       # TypeScript configuration

Contributing

Contributions are welcome! Please ensure you:

  1. Write clear commit messages
  2. Add appropriate documentation
  3. Follow the existing code style
  4. Add/update tests as needed
Publicly Shared Threads0

Discover shared experiences

Shared threads will appear here, showcasing real-world applications and insights from the community. Check back soon for updates!

Share your threads to help others
Related MCPs5
  • TxtAi Memory Vector Server
    TxtAi Memory Vector Server

    Model Context Protocol (MCP) server implementation for semantic search and memory management using T...

    Added May 30, 2025
  • Powertools MCP Search Server
    Powertools MCP Search Server

    Enables LLMs to search through AWS Lambda Powertools documentation across multiple runtimes (Python,...

    2 tools
    Added May 30, 2025
  • sanderkooger-mcp-server-ragdocs
    sanderkooger-mcp-server-ragdocs

    An MCP server implementation that provides tools for retrieving and processing documentation through...

    Added May 30, 2025
  • mem0 MCP Server
    mem0 MCP Server

    A TypeScript implementation of the Model Context Protocol server that enables creation, management, ...

    Added May 30, 2025
  • Gemini MCP Image Generation Server
    Gemini MCP Image Generation Server

    A Model Context Protocol server that provides image generation capabilities using Google's Gemini 2 ...

    1 tools
    Added May 30, 2025