Comment Stripper MCP

Comment Stripper MCP is a dedicated Model Context Protocol server designed to cleanse source code of comments across a wide spectrum of programming languages. By stripping annotations, documentation blocks, and other non-executable text, it produces leaner code snippets that are easier for AI assistants to parse and analyze. This is particularly valuable when feeding code into models that are sensitive to noise or when preparing data for downstream tooling such as linters, formatters, or static analysis engines.

The server accepts three primary input modes: single files, entire directories (recursively traversing sub‑folders), and raw text strings. Internally it leverages regular‑expression patterns tuned to the comment syntax of each supported language—JavaScript, TypeScript, Vue, CSS/SCSS/LESS, HTML, Python, Java, C#, C++, Ruby, and PHP. The result is a clean code payload that preserves original formatting while eliminating any comment delimiters or content. Because the output is still syntactically valid, developers can immediately use it in code generation pipelines or feed it back into their editors for further transformation.

Key capabilities include a robust MCP‑compliant API, configurable through environment variables, and comprehensive logging with multiple verbosity levels. The server is built on Node.js and TypeScript, ensuring type safety and maintainability. It also incorporates performance optimizations such as chunked file processing, configurable worker pools for concurrent tasks, and memory limits to prevent resource exhaustion. Security is addressed with API authentication mechanisms that restrict access to authorized clients, making it suitable for production deployments.

Typical use cases span from preparing code samples for AI‑driven documentation tools—where comments might clutter the generated prose—to cleaning up repositories before running automated code quality checks. In a CI/CD pipeline, Comment Stripper MCP can be invoked as an early step to strip comments from source files before they are passed to static analyzers or formatters, reducing false positives and speeding up the build. Developers integrating Claude or other MCP‑compatible assistants can simply add a “strip comments” step in their workflow, ensuring that the model receives only the executable logic it needs to reason about.

What sets this server apart is its blend of language coverage, TDD‑driven reliability, and MCP readiness. By exposing a clean, well‑documented interface, it allows AI assistants to treat comment removal as a first‑class operation—enhancing clarity in code understanding, reducing model hallucinations related to comment content, and streamlining downstream tooling.