MCP Code Analyzer

The MCP Code Analyzer is a dynamic code‑analysis server that bridges the gap between an AI assistant and a live codebase. Its core purpose is to intelligently adapt project changes, ensuring that when a file is modified or a new feature is added, all dependent modules and references are automatically updated. This reduces the risk of stale imports, broken links, or inconsistent API contracts that often plague large projects when changes are made in isolation.

At its heart, the server exposes a suite of analysis tools that operate at both the project and file levels. Developers can request an XML tree of the entire repository, obtain statistical summaries (e.g., line counts, complexity metrics), and identify the technology stack in use. For individual files, the server can list imports, locate reference sites, and even suggest refactorings. These insights are delivered through the MCP protocol so that Claude Desktop or any compliant AI client can surface them directly in a conversational UI, allowing developers to ask questions like “Which modules import ?” or “Show me the project’s dependency graph.”

Beyond passive analysis, MCP Code Analyzer offers file operations and code modification capabilities. With a simple command, the server can create dated backups before performing structural changes, or apply line‑by‑line edits suggested by the AI. Although these features are flagged as experimental, they enable a powerful workflow: an assistant proposes a refactor, the server executes it safely, and the updated code is immediately available for further review or testing. Because all changes are version‑controlled, developers can roll back if something goes wrong.

Typical use cases include automated refactoring, where a naming convention change must propagate across hundreds of files, or dependency cleanup after a library upgrade. In continuous integration pipelines, the server can run static‑analysis checks and report violations back to the AI, which then generates remediation suggestions. For onboarding new contributors, the assistant can walk through the project structure and highlight key modules, leveraging the server’s comprehensive overview.

What sets MCP Code Analyzer apart is its tight integration with AI workflows and the safety nets it provides. By exposing file‑level operations as MCP tools, developers can keep their IDE or command line untouched while still benefiting from the AI’s contextual understanding. The server’s modular design means new analysis or modification tools can be added without altering the client, making it a flexible companion for any code‑centric AI project.