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Advanced Code Analysis MCP Server

@ItamarZand88

代码仓库

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stdio

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ItamarZand88

关于 Advanced Code Analysis MCP Server

Advanced Code Analysis MCP Server with Knowledge Graph and AI Insights - TypeScript implementation with Neo4j backend

#advanced-code-analysis#code-analysis#ai-insights

Advanced Code Analysis MCP Server 配置

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该服务器的 README 中没有可解析的 MCP 配置块，请前往代码仓库查看安装说明。

代码仓库

Advanced Code Analysis MCP Server 工具

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工具是从 README 中自动提取的。维护者可以在 ## Tools 标题下列出工具,即可填充这部分内容。

概览

What is Advanced Code Analysis MCP Server?

An MCP server that combines Neo4j knowledge graphs, AI-powered analysis (GPT‑4/Claude), and advanced static analysis to analyze large‑scale code projects. It is designed for developers and teams needing deep architectural insights, security analysis, and quality metrics.

How to use Advanced Code Analysis MCP Server?

Use the analyzeRepository method with a git URL and branch, then query the resulting knowledge graph using natural language. Setup is via Docker (docker‑compose up -d) or local installation (npm install && npm run build && npm start). Configure API keys and Neo4j settings in a .env file.

Key features of Advanced Code Analysis MCP Server

  • AI‑Powered Analysis with GPT‑4/Claude for advanced insights
  • Advanced Knowledge Graph with Neo4j for complex relationships
  • Natural Language Queries with no learning curve
  • Deep Architectural Analysis and Automated Security Analysis
  • Parallel Processing for large projects (up to 10M lines in 30 min)
  • Comprehensive Quality Metrics and monitoring dashboards

Use cases of Advanced Code Analysis MCP Server

  • Enterprise Code Audit: analyze main system for security issues and code smells
  • Developer Onboarding: show core components and how they interact
  • Refactoring Planning: find tightly coupled components and suggest where to break dependencies
  • Performance Analysis: find bottlenecks and inefficient algorithms

常见问题

1

What are the system requirements?

Node.js 18+, Docker & Docker Compose, 8GB+ RAM, 100GB+ disk space.

2

How do I set up the server?

Copy `.env.example` to `.env`, edit settings, then run `docker-compose up -d` or install locally with `npm install && npm run build && npm start`.

3

Which AI providers are supported?

OpenAI (GPT‑4) and Anthropic (Claude). Set `AI_PROVIDER`, `AI_MODEL`, and the corresponding API keys in your `.env`.

4

How can I monitor performance and health?

Access Neo4j Browser at http://localhost:7474, Grafana Dashboard at http://localhost:3001, Prometheus Metrics at http://localhost:9091, and Health Check at http://localhost:3000/health.

5

What transports or authentication are used?

The server exposes an HTTP API (health check on port 3000). AI and Neo4j credentials are configured via environment variables (`OPENAI_API_KEY`, `ANTHROPIC_API_KEY`, `NEO4J_PASSWORD`).

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