XRAY MCP – AST-grep wrapped in a tiny server for code-aware AI
Hi HN, I built XRAY MCP after discovering that AI assistants were scanning my projects with plain grep and guessing. I tried direct tree-sitter integration and language servers; both felt heavy for a lightweight tool. ast-grep hit a sweet spot: syntax-aware search in a single binary. XRAY MCP wraps it behind three endpoints—map, find, impact—so a model (or human) can answer questions like “what breaks if I change this function?” on demand. It’s stateless, supports Python/JS/TS/Go, and installs quickly. Repo: https://github.com/srijanshukla18/xray Would love…
What it does
In the maker’s words, at launch
Hi HN, I built XRAY MCP after discovering that AI assistants were scanning my projects with plain grep and guessing. I tried direct tree-sitter integration and language servers; both felt heavy for a lightweight tool. ast-grep hit a sweet spot: syntax-aware search in a single binary. XRAY MCP wraps it behind three endpoints—map, find, impact—so a model (or human) can answer questions like “what breaks if I change this function?” on demand. It’s stateless, supports Python/JS/TS/Go, and installs quickly. Repo: https://github.com/srijanshukla18/xray Would love to know your thoughts!
Does the same job
all alternatives →- IWI wrote a book on GNU grep and ripgrep2019 · ▲182
My book on "GNU grep and ripgrep" is free to download today and tomorrow [1][2] Code snippets, example files and sample chapters are available on GitHub [3] The book uses plenty of examples and regular expressions are also covered from scratch. The book is suitable for beginners as well as serves as a reference. Hope you find it useful, I would be grateful for your feedback and suggestions. I used pandoc+xelatex [4] to generate the pdf. [1] https://gumroad.com/l/gnugrep_ripgrep [2] https://leanpub.com/gnugrep_ripgrep [3]…
- SMSourcerer – MCP for semantic code search that reduces token waste2025 · github.com · ▲9
I built this because I got tired of watching Claude Code read through massive files just to find a few functions. Sourcerer lets AI agents search code semantically and grab exactly the code chunks they need instead of burning tokens on whole files. It uses tree-sitter to parse your codebase and creates a searchable index. So instead of "read auth.py (538 lines)", an agent can search for "user authentication logic" and get back just the relevant functions. Demo: https://asciinema.org/a/736638 GitHub: https://github.com/st3v3nmw/sourcerer-mcp
- THTree-hugger-JS: CSS selectors for JavaScript AST analysis and MCP2025 · ▲7
I built a library that lets you find code patterns using familiar CSS-like selectors, then connected it to Claude via MCP so AI assistants can understand and refactor codebases. The Approach // Find code patterns with intuitive selectors: const asyncFunctions = tree.findAll('function[async]'); const todoComments = tree.findAll('comment[text="TODO"]'); const reactHooks = tree.hooks(); // Built-in React support // Chain smart transformations: tree.transform() .rename('oldFunction', 'newFunction') .removeUnusedImports() .toString(); Key Features - CSS-like…
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