Advanced Code Analysis MCP Server

@ItamarZand88

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Basic information

About 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 config

No standard config provided

This server doesn't expose a parseable MCP config block in its README. See the repository for install instructions.

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Advanced Code Analysis MCP Server tools

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Overview

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

Frequently asked questions

What are the system requirements?

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

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`.

Which AI providers are supported?

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

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.

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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