이 프로젝트는 Roo Code Memory Bank 시스템의 핵심 기능을 Model Context Protocol (MCP) 서버로 구현합니다. 그것은 AI 조수가 구성 MCP 도구를 사용하여 파일 기반 메모리 은행과 상호 작용하여 세션 전반에 걸쳐 프로젝트 컨텍스트를 유지 할 수 있습니다.

특징

이 MCP 서버는 다음과 같은 도구를 제공합니다:

• initialize_memory_bank: memory-bank/] 디렉토리 및 표준 .md]파일(productContext.md], activeContext.md], progress.md, [decisionLog.md], systemPatterns.md]]) 초기 템플릿.
• 입력: (선택) { "project_brief_content": string }
• 출력 *: { "status": "success" | "error", "messages"?: string[], "message"?: string } *check_memory_bank_status: memory-bank/ 디렉토리가 존재하고 .md] 파일을 나열하면 확인합니다.
• 입력 * 입력 *: {}
• 출력 *: { "exists": boolean, "files": string[] }
• read_memory_bank_file: 지정된 메모리 은행 파일의 전체 내용을 읽습니다.
• 입력*: { "file_name": string } Output: { "content": string } 또는 오류 객체. *append_memory_bank_entry: 지정된 파일에 새, 타임스탬프 된 항목, 특정 마크 다운 헤더의 밑에 선택적으로.

Roo Code Memory Bank MCP Server

This project implements the core functionality of the Roo Code Memory Bank system as a Model Context Protocol (MCP) server. It allows AI assistants to maintain project context across sessions by interacting with a file-based memory bank using structured MCP tools.

Features

This MCP server provides the following tools:

• initialize_memory_bank: Creates the memory-bank/ directory and standard .md files (productContext.md, activeContext.md, progress.md, decisionLog.md, systemPatterns.md) with initial templates.
  • Input: (Optional) { "project_brief_content": string }
  • Output: { "status": "success" | "error", "messages"?: string[], "message"?: string }
• check_memory_bank_status: Checks if the memory-bank/ directory exists and lists the .md files within it.
  • Input: {}
  • Output: { "exists": boolean, "files": string[] }
• read_memory_bank_file: Reads the full content of a specified memory bank file.
  • Input: { "file_name": string }
  • Output: { "content": string } or error object.
• append_memory_bank_entry: Appends a new, timestamped entry to a specified file, optionally under a specific markdown header. Creates the file if it doesn’t exist.
  • Input: { "file_name": string, "entry": string, "section_header"?: string }
  • Output: { "status": "success" | "error", "message": string }

Prerequisites

• Node.js (v18 or later recommended)
• npm (usually included with Node.js)
• An MCP client environment (like the one used by Cline) capable of managing and launching MCP servers.

Installation

1. Clone the repository:

   git clone https://github.com/IncomeStreamSurfer/roo-code-memory-bank-mcp-server.git
   cd roo-code-memory-bank-mcp-server
   

2. Install dependencies:

   npm install
   

3. Build the project:

   npm run build
   

   This compiles the TypeScript code into JavaScript in the dist/ directory.

Configuration (for Cline MCP Client)

To make this server available to your AI assistant (like Cline), you need to add its configuration to your MCP settings file (e.g., cline_mcp_settings.json).

Find the mcpServers object in your settings file and add the following entry:

{
  "mcpServers": {
    // ... other server configurations ...

    "roo-code-memory-bank-mcp": {
      "autoApprove": [
        "initialize_memory_bank",
        "check_memory_bank_status",
        "read_memory_bank_file",
        "append_memory_bank_entry"
      ],
      "disabled": false,
      "timeout": 60,
      "command": "node", // Or "cmd.exe" with "/c node ..." on Windows if needed
      "args": [
        // IMPORTANT: Replace this path with the actual absolute path
        // to the compiled index.js file on your system