Code Analyzer MCP Server

Code Analyzer MCP Server Overview

The Code Analyzer MCP Server bridges the gap between human developers and automated static‑analysis tooling by exposing a unified interface for linting, error detection, and automated fixes across multiple languages. By running ESLint, HTMLHint, Stylelint, and Pyright under a single MCP endpoint, it eliminates the need to invoke each tool separately or manage complex configuration pipelines. This streamlines code quality workflows, especially in environments where AI assistants like Claude are used to review or refactor code on the fly.

When a developer asks an AI assistant to “check my file for bugs,” the server’s tool parses the requested path, automatically detects the language if not supplied, and runs the appropriate linter. The result is a structured list of issues—each with an identifier, severity, and message—that the assistant can present back to the user. If the user wants immediate remediation, setting triggers the underlying linters’ auto‑formatting or quick‑fix capabilities, producing a cleaned file without further intervention. This capability is especially valuable for rapid prototyping or continuous integration pipelines where quick feedback loops are critical.

Beyond basic analysis, the server offers fine‑grained control through and . After an initial scan, a developer can select specific issue IDs to patch, allowing selective application of fixes while preserving intentional code patterns. The endpoint provides contextual guidance for each problem, enabling the AI to offer human‑readable explanations or code snippets that illustrate how to resolve an issue. These features make the server a powerful partner for pair‑programming sessions, code reviews, and educational settings where developers need clear, actionable feedback.

Typical use cases include:

• Continuous Integration: Integrate the server into CI/CD pipelines to automatically lint and fix code before merge, ensuring a consistent quality gate.
• Developer Onboarding: New contributors can quickly learn project standards by receiving immediate, language‑specific linting feedback from an AI tutor.
• Code Refactoring: During large refactors, the server can flag legacy patterns and suggest modern alternatives, accelerating cleanup efforts.
• Automated Documentation: The analysis results can feed into documentation generators that highlight code quality metrics.

Integration with AI workflows is straightforward: an assistant invokes the MCP tools via standard calls, receives structured JSON responses, and can weave the findings into natural‑language explanations or code modifications. Because all language support is bundled, developers can write a single prompt that works across the full stack—JavaScript, TypeScript, HTML, CSS, and Python—without worrying about toolchain differences.

The server’s standout advantages are its language‑agnostic abstraction, the ability to auto‑fix while preserving intentional code, and the provision of human‑readable fix suggestions. These features reduce friction for developers who rely on AI assistants, turning static analysis from a tedious command‑line task into an interactive, context‑aware conversation.