Advanced Code Analysis MCP Server

Advanced Code Analysis MCP Server

The Advanced Code Analysis MCP server tackles a core challenge for modern software teams: extracting actionable intelligence from sprawling codebases without drowning in noise. By marrying a Neo4j knowledge graph with AI‑powered static analysis, it turns raw source files into an interactive map of entities, relationships, and quality metrics. Developers can query this graph in natural language, instantly surfacing architectural patterns, hidden dependencies, or potential security gaps that would otherwise require hours of manual inspection.

At its heart, the server ingests a repository and builds a multi‑layered graph that captures files, functions, classes, modules, and even test coverage. Parallel workers process tens of thousands of lines in under half an hour, meeting performance targets of 10 M LOC in <30 min. The AI layer (GPT‑4/Claude) augments the graph with higher‑level insights—identifying complex functions, suggesting refactor points, or flagging suspicious code paths—while the Neo4j backend guarantees sub‑second query responses for most requests. Security analysis runs automatically, surfacing vulnerabilities that align with OWASP or industry best practices.

Key capabilities include:

• Natural‑language querying: Ask “Show me the most complex functions” or “Which files lack tests?” and receive structured answers without writing Cypher.
• Deep architectural analysis: Visualize component interactions, detect circular dependencies, and surface architectural smells.
• Parallel processing: Scale to large monorepos or microservice collections, with configurable workers and concurrent jobs.
• Security & quality metrics: Integrated scans produce actionable reports on code smells, test coverage gaps, and potential exploits.
• Extensible API: Expose complexity, dependency, quality, and security data to downstream tools or dashboards.

In practice, enterprises use it for end‑to‑end code audits, ensuring compliance and security before release. New hires can onboard faster by exploring the core component graph rather than reading documentation alone. Refactoring teams leverage tight‑coupling detection to plan modularization, while performance engineers hunt bottlenecks through graph‑based algorithm analysis. By embedding the server into CI/CD pipelines or IDE extensions, teams gain continuous visibility over code health, turning static analysis from a one‑off task into an ongoing, AI‑driven conversation about the codebase.