# 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](https://modelcontextprotocol.io/introduction) 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](https://modelcontextprotocol.io/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 ```bash # 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 ```bash npm run dev ``` ### Production Mode ```bash 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: ```json { "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:** ```javascript 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:** ```javascript 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:** ```javascript 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:** ```javascript 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: 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 ## License MIT