memory-bank-MCP

Memory Bank MCP

Structured project knowledge management for LLMs via Model Context Protocol (MCP)

Note: This is not a traditional Node.js application. Memory Bank MCP is an MCP server—a component in the Model Context Protocol ecosystem. It exposes project knowledge to LLM-powered agents and tools using a standardized protocol, enabling seamless integration with AI clients (e.g., Claude Desktop, IDEs, or custom LLM agents).

MCP is an open protocol that standardizes how applications provide context to LLMs. Think of MCP like a USB-C port for AI: it provides a universal way to connect AI models to data sources and tools, both locally and remotely. MCP enables:

• Plug-and-play integrations between LLMs, data, and tools
• Switching between LLM providers with minimal friction
• Secure, modular architecture for building AI workflows

Learn more: MCP Introduction

Memory Bank MCP is an MCP server that helps teams create, manage, and access structured project documentation. It generates and maintains interconnected Markdown documents capturing all aspects of project knowledge, from high-level goals to technical details and daily progress. It is designed to be accessed by MCP-compatible clients and LLM agents.

• AI-Generated Documentation: Uses Gemini API to generate and update project documentation
• Structured Knowledge System: Maintains six core document types in a hierarchical structure
• MCP Server: Implements the Model Context Protocol for integration with LLM agents and tools
• Customizable Storage: Choose where your Memory Bank directory is created
• Document Templates: Pre-defined templates for project brief, product context, system patterns, etc.
• AI-Assisted Updates: Update documents manually or regenerate them with AI
• Advanced Querying: Search across all documents with context-aware relevance ranking

# Clone the repository
git clone https://github.com/tuncer-byte/memory-bank-mcp.git
cd memory-bank-mcp

# Install dependencies
npm install

# (Optional) Create .env file with your Gemini API key
echo "GEMINI_API_KEY=your_api_key_here" > .env

Note: Memory Bank MCP is intended to be run as an MCP server, not as a standalone app. You typically launch it as part of an MCP workflow, and connect to it from an MCP-compatible client (such as Claude Desktop or your own LLM agent).

npm run dev

npm run build
npm run start

To connect Memory Bank MCP to your MCP client, add the following to your mcp.json configuration:

{
  "memoryBank": {
    "command": "node",
    "args": ["/path/to/memory-bank-mcp/dist/index.js"],
    "env": {
      "GEMINI_API_KEY": "your_gemini_api_key_here"
    }
  }
}

Replace /path/to/memory-bank-mcp/dist/index.js with the absolute path to your built file, and add your Gemini API key if needed.

Memory Bank MCP provides the following tools via the Model Context Protocol:

Creates a new Memory Bank structure with all document templates.

Parameters:

• goal (string): Project goal description (min 10 characters)
• geminiApiKey (string, optional): Gemini API key for document generation
• location (string, optional): Absolute path where memory-bank folder will be created

Example:

await callTool({
  name: "initialize_memory_bank",
  arguments: {
    goal: "Building a self-documenting AI-powered software development assistant",
    location: "/Users/username/Documents/projects/ai-assistant"
  }
});

Updates a specific document in the Memory Bank.

Parameters:

• documentType (enum): One of: projectbrief, productContext, systemPatterns, techContext, activeContext, progress
• content (string, optional): New content for the document
• regenerate (boolean, default: false): Whether to regenerate the document using AI

Example:

await callTool({
  name: "update_document",
  arguments: {
    documentType: "projectbrief",
    content: "# Project Brief\n\n## Purpose\nTo develop an advanced and user-friendly AI..."
  }
});

Searches across all documents with context-aware relevance ranking.

Parameters:

• query (string): Search query (min 5 characters)

Example:

await callTool({
  name: "query_memory_bank",
  arguments: {
    query: "system architecture components"
  }
});

Exports all Memory Bank documents.

Parameters:

• format (enum, default: "folder"): Export format, either "json" or "folder"
• outputPath (string, optional): Custom output path for the export

Example:

await callTool({
  name: "export_memory_bank",
  arguments: {
    format: "json",
    outputPath: "/Users/username/Documents/exports"
  }
});

Memory Bank organizes project knowledge into six core document types:

1. Project Brief (projectbrief.md): Core document defining project objectives, scope, and vision
2. Product Context (productContext.md): Documents product functionality from a user perspective
3. System Patterns (systemPatterns.md): Establishes system architecture and component relationships
4. Tech Context (techContext.md): Specifies technology stack and implementation details
5. Active Context (activeContext.md): Tracks current tasks, open issues, and development focus
6. Progress (progress.md): Documents completed work, milestones, and project history

MIT