Memory Bank 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).

What is Model Context Protocol (MCP)?

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

About Memory Bank MCP

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.

Features

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

Installation

# 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

Usage

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).

Development Mode

npm run dev

Production Mode

npm run build
npm run start

MCP Integration

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.

MCP Tools Exposed by Memory Bank

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

`initialize_memory_bank`

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"
  }
});

`update_document`

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..."
  }
});

`query_memory_bank`

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"
  }
});

`export_memory_bank`

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"
  }
});

Document Types

Memory Bank organizes project knowledge into six core document types:

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

License

MIT

Deploy Server

HTTP connection URL

security – no known vulnerabilities

license - not found

quality - confirmed to work

How are these scores calculated?

Tools

An MCP server that helps teams create, manage, and access structured project documentation through six core document types, leveraging AI to generate comprehensive project knowledge management.

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Memory Bank MCP

Memory Bank MCP

What is Model Context Protocol (MCP)?

About Memory Bank MCP

Features

Installation

Usage

Development Mode

Production Mode

MCP Integration

MCP Tools Exposed by Memory Bank

`initialize_memory_bank`

`update_document`

`query_memory_bank`

`export_memory_bank`

Document Types

License

Tools

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Vibe Coder MCP

Trapper Keeper MCP

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