记忆库MCP

通过模型上下文协议（MCP）实现LLM的结构化项目知识管理

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注： 这不是传统的Node.js应用。内存银行MCP是一个MCP服务器——模型上下文协议生态系统中的一个组件。它通过标准化协议向LLM驱动的代理和工具展示项目知识，实现与AI客户端（如Claude Desktop、IDE或自定义LLM代理）的无缝集成。

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什么是模型上下文协议（MCP）？

MCP是一种开放协议，规范了应用程序如何为LLM提供上下文。可以把MCP想象成AI的USB-C端口：它提供了一种通用方式，将AI模型与本地和远程的数据源和工具连接起来。MCP支持：

• 即插即用集成 LLM、数据与工具之间
• **在LLM提供者之间切换，阻力最小
• 安全、模块化架构用于构建AI工作流程

了解更多：MCP简介

关于记忆库MCP

Memory Bank MCP 是一个MCP服务器，帮助团队创建、管理和访问结构化的项目文档。

Memory Bank MCP

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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).

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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).