mcp-project-overview by Pengguancheng - MCP Server

MCP Project Overview

MCP Server for creating code project file architecture and summary using the Model Context Protocol (MCP).

Description

This is a Node.js application built with TypeScript that implements an MCP server to help analyze and summarize code project architecture. It uses the @modelcontextprotocol/sdk to provide resources and tools for project analysis.

Features

Project Structure Resource: Get a structured view of a project's file hierarchy
File Analysis Tool: Analyze the content and purpose of individual files
Project Summary Tool: Generate a comprehensive summary of a project including its structure and package information
LangChain Integration: Utilize LangChain for advanced language model capabilities
Chroma Vector Database: Store and retrieve vector embeddings for semantic search

Prerequisites

Node.js (v18 or higher)
npm (v6 or higher)
OpenAI API key (for LangChain and vector embeddings)
Chroma DB server (optional, defaults to http://localhost:8000)

Installation

Clone the repository:

git clone https://github.com/Pengguancheng/mcp-project-overview.git
cd mcp-project-overview

Install dependencies:
```
npm install
```

MCP Server 配置

要在您的編輯器或開發環境中整合 MCP Server，請使用以下配置：

{
  "mcpServers": {
    "chroma": {
      "command": "node",
      "args": [
        "dist/index.js"
      ],
      "env": {
        "OPENAI_API_KEY": "your-openai-api-key-here",
        "PROJECT_NAME": "your-project-name"
      }
    }
  }
}

環境變量說明

OPENAI_API_KEY: 您的 OpenAI API 密鑰，用於生成嵌入和項目摘要
PROJECT_NAME: 項目名稱，用作 Chroma 集合名稱
CHROMA_URL: (可選) Chroma 數據庫服務器 URL，默認為 http://localhost:8000

MCP 使用範例

添加代碼文檔到向量數據庫

// 添加類文檔
await mcpClient.callTool("vector-add", {
  type: "class",
  name: "UserRepository",
  namespace: "app.repositories",
  text: "負責用戶數據的 CRUD 操作，包含創建、讀取、更新和刪除用戶的方法。",
  projectName: "my-project",
  filePath: "src/repositories/UserRepository.ts",
  summary: "用戶數據訪問層",
  references: ["src/models/User.ts", "typeorm"]
});

語義搜索代碼實體

// 自然語言查詢
const results = await mcpClient.callTool("vector-search", {
  query: "用戶認證相關的代碼",
  projectName: "my-project",
  limit: 5
});

// 按類型過濾
const classResults = await mcpClient.callTool("vector-search", {
  query: "數據庫操作",
  type: "class",
  projectName: "my-project"
});

// 按引用關係過濾
const refResults = await mcpClient.callTool("vector-search", {
  query: "用戶管理",
  references: ["src/models/User.ts"],
  projectName: "my-project"
});

生成項目概覽

await mcpClient.callTool("summary-code", {
  projectDir: "/path/to/your/project",
  targetDir: "src",
  outputFile: "docs/overview.md",
  summaryType: "overview"
});

清除向量數據庫

await mcpClient.callTool("vector-clear", {
  projectName: "my-project"
});

使用資源查詢

// 使用資源進行自然語言查詢
const resourceResult = await mcpClient.getResource("chroma://query/視頻處理相關的代碼");