ContextMemory: MCP Server & 3D Visualizer 🧠

An ultra-lightweight, lightning-fast Model Context Protocol (MCP) Server and 3D Visualizer designed to act as a plug-and-play package. You can use this repository to instantly create your own custom MCP memory server for any project—simply by plugging in your own Neo4j Aura database credentials!

🌟 Features

• Plug-and-Play MCP Server: Instantly give your AI coding assistants (Antigravity, OpenCode, Claude Code, Cursor, etc.) a persistent, shared memory via search_memory, add_memory, and delete_memory tools.

• Lazy-Loaded PyTorch: Instant server boot times (less than 4s). AI models only load into RAM when a memory operation is explicitly requested.

• Realtime 3D Visualizer: A sleek local web application that maps your project's memory as a glowing, interactive neural network.

• Neo4j Aura Ready: Directly offloads graph storage and vector queries to Neo4j Cloud, meaning it works on any machine without local database installation.

• 100% Secure: Credentials are kept entirely out of the code using .env files, ensuring your database passwords are never leaked to GitHub!

Related MCP server: memory-vault

🚀 Quick Start (Local Setup)

1. Clone the Repository

   git clone https://github.com/mmyahya29/MCP-Visualizer.git
   cd MCP-Visualizer

2. Configure your Database Credentials Rename the .env.example file to .env and insert your Neo4j Cloud credentials:

   NEO4J_URI=neo4j+s://...
   NEO4J_USERNAME=neo4j
   NEO4J_PASSWORD=your_password

3. Launch the Visualizer!

   • Windows: Double-click run.bat

   • Mac/Linux: Run ./run.sh

   This automatically sets up your Python virtual environment, installs the requirements, and opens the 3D visualizer at http://localhost:8000.

🤖 Connecting to your AI Agents (MCP Setup)

To give your AI coding agents access to this brain, configure them to use this repository as a local MCP server.

For Antigravity: Add the following to your ~/.gemini/config/mcp_config.json:

"mcpServers": {
  "foster-memory": {
    "command": "/absolute/path/to/MCP-Visualizer/venv/Scripts/python.exe",
    "args": [
      "/absolute/path/to/MCP-Visualizer/server.py"
    ]
  }
}

(Mac/Linux users: change venv/Scripts/python.exe to venv/bin/python)

For OpenCode: Add the following to your ~/.config/opencode/opencode.json:

"mcp": {
  "foster-memory": {
    "type": "local",
    "command": [
      "/absolute/path/to/MCP-Visualizer/venv/Scripts/python.exe",
      "/absolute/path/to/MCP-Visualizer/server.py"
    ],
    "enabled": true
  }
}

🌐 Deploying to Vercel

If you want to host your 3D Visualizer permanently on the internet:

1. Copy the contents of the VercelVisualizer folder into a new GitHub repository.

2. Go to Vercel.com and deploy that specific repository.

3. In your Vercel Dashboard, add your NEO4J_URI, NEO4J_USERNAME, and NEO4J_PASSWORD environment variables.

Built to make your AI tools smarter.