Alternatives
Products that do what XRAY MCP – AST-grep wrapped in a tiny server for code-aware AI does
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…
- 1IW
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]…
2019
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- 4SM
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
2025 · github.com
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- 6TH
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…
2025
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- 8OS
I built a zero-configuration tool for automatically exposing FastAPI endpoints as Model Context Protocol (MCP) tools, open to collabs and contributions!
2025 · github.com
- 9MO
Hey HN - we built Morphik MCP to solve a common problem: finding specific information across scattered technical docs. We've experimented with GraphRAG, ColPali, contextual embeddings, and more. MCP emerged as the solution that unifies these approaches. Features: - Multimodal search across text, diagrams, and videos - Natural language knowledge base management - Fully open-source with responsive support What sets MCP apart is its ability to return images (including diagrams) directly to the MCP client. Users have applied it to search over data ranging from blood tests to patents, and we use…
2025 · docs.morphik.ai
- 10SC
Hey HN! We (Stephan and Thomas) recently open-sourced Semble. We kept running into the same problem while using Claude Code on large codebases: when the agent can't find something directly, it falls back to grep, reading full files or launching subagents. This uses a lot of tokens, and often still misses the relevant code. There are existing tools for this, but they were either too slow to index on demand, needed API keys, or had poor retrieval quality. So we built Semble. It combines static Model2Vec embeddings (using our latest static model: potion-code-16M) with BM25, fused via RRF and…
May 2026 · github.com
- 11HS
Releasing ColGREP today, made using our open-source multi-vector database, it support grep like filtering while adding an extra input to rank output of grep based on semantic similarity. ColGREP is packed with strong code retrieval models we designed and run 100% locally.
Feb 2026 · github.com
- 12SF
Hey HN! We've just open-sourced Semble, a fast and accurate code search library built for agents. We're also releasing potion-code-16M, a small code-specialized static embedding model that powers it. Most embedding-based code search methods are either too slow to index on demand or need GPU infrastructure, while grep-style retrieval methods often cannot find the relevant content. Semble combines the speed and quality benefits of both, so agents waste less time and fewer tokens exploring. Main features: - Fast: indexes a full codebase in ~250 ms and answers queries in ~1.5 ms, all on CPU…
Apr 2026 · github.com
- 13OP
Hello HN, Pietro here! I've been really excited to see the recent buzz around MCP and all the cool things people are building with it. Though, the fact that you can use it only through desktop apps really seemed wrong and prevented me for trying most examples, so I wrote a simple client, then I wrapped into some class, and I ended up creating a python package that abstracts some of the async uglyness. You need: * one of those MCPconfig JSONs * 6 lines of code and you can have an agent use the MCP tools from python. The structure is simple: an MCP client creates and manages the connection and…
2025 · github.com
- 14SS
I built sqry, a local code search tool that works at the semantic level rather than the text level. The motivation: ripgrep is great for finding strings, but it can't tell you "who calls this function", "what does this function call", or "find all public async functions that return Result". Those questions require understanding code structure, not just matching patterns. sqry parses your code into an AST using tree-sitter, builds a unified call/ import/dependency graph, and lets you query it: sqry query "callers:authenticate" sqry query "kind:function AND visibility:public AND…
Mar 2026 · sqry.dev
- 15OS
Hello HN, I built this because I wanted to give Claude Desktop access to my Notion workspace without running a flaky local Python script via stdio. This is a Node.js/Express implementation of the Model Context Protocol (MCP) that uses SSE (Server-Sent Events) for transport. It’s designed to be stateless and deployable as a container (I'm hosting it on Apify, but it works anywhere with Node). The Stack: TypeScript + Express @modelcontextprotocol/sdk Zod for input validation Bearer Auth for security (since it exposes an HTTP endpoint) Capabilities: It allows the LLM to search pages,…
Dec 2025 · github.com
- 16WT
We just launched a small project I'm really proud of — a turbo Database MCP server! https://centralmind.ai - Connect your database to Cursor or Windsurf in just a few clicks. - Chat with PostgreSQL, MSSQL, ClickHouse, Elasticsearch, and more. - Query huge Parquet files instantly with DuckDB in-memory mode. - No downloads, no setup headaches. Short video: https://youtu.be/BboQtxen9tA Built on top of our open-source MCP Database Gateway: https://github.com/centralmind/gateway Note: You’ll need to provide connection strings to your databases. For…
2025 · centralmind.ai
- 17AC
For the past couple of months, I’ve been building a tool that enables natural language search over large codebases using Tree-Sitter for syntax parsing and Qdrant for vector-based retrieval. https://app.repogram.com ### How It Works - Tree-Sitter is used to parse syntax trees and extract high-quality vector embeddings of code. - These embeddings are stored in Qdrant, enabling fast similarity search across your entire repo. - A combination of re-ranking processes refine search results, producing highly relevant answers to code-related questions. The results have been incredibly…
2025 · app.repogram.com
- 18MS
Hey, I'm Nick from Nutrient, I want to share our newly released MCP Server that enables document workflows using natural language — things like redacting, merging, signing, converting formats, or extracting data. While many MCP servers have traditionally been developer-focused, we recognized that the technology could be highly effective in promoting the adoption of tools that are often hidden from end-user interfaces. We’re really interested to see if this side of the protocol could continue to mature. One thing we struggled with was the inability to receive documents from the client (no…
2025 · github.com
- 19CD
Hey folks, I've been building AI agents that need to talk to various APIs, and I got tired of writing custom integrations for every service. So I built the MCP-OpenAPI Server to solve this problem! It's a simple bridge that lets AI agents discover and use our existing OpenAPI endpoints through the Model Context Protocol. No need to write custom code for each service - just point it at the OpenAPI specs, choose which endpoints to expose, and you're good to go. What makes this different from other MCP servers is that it uses SSE transport instead of stdio, making it work well for multi-tenant…
2025 · github.com
- 20LF
We built a no/low-code tool that lets you spin up MCPs from a single prompt. MCPs give LLMs access to tools, data, and actions—but they’re hard to build and deploy. Our tool abstracts that: describe what you want, and it auto-generates and hosts the necessary components. No UI flows, no manual chaining—just prompt and go. Examples: • Pull email, parse a DocSend, check Reddit, draft reply • Extract data from a niche site + send a Slack alert • Combine tools without writing glue code Live demo: https://www.youtube.com/watch?v=4uCiaQrgfoE Built over a weekend after getting…
2025 · generatemcp.com
- 21MF
Hi, I am Anthony. Every token your filesystem tools consume is context the model cannot use for reasoning. Most MCP file servers are O(file size) on every operation: reads return the whole file, edits rewrite the whole file. The context window fills up before the agent gets anything meaningful done, and the problem compounds silently as your files grow. Chisel makes edits O(diff) and reads O(match). The agent sends a unified diff instead of a full rewrite, and queries with grep or sed instead of reading entire files. On a 500-line file this is nearly two orders of magnitude less context per…
Mar 2026 · github.com
- 22CI
Code Index MCP is an MCP server that indexes codebases and provides search capabilities to LLMs. Supports 50+ file types with automatic indexing, regex/fuzzy search, code analysis, and real-time file monitoring. LLMs can search your entire project, find files with glob patterns, analyze code structure (functions, classes, imports), and get automatic updates when files change. Eliminates the need to manually copy files or explain project structure to overcome context limits. Built with Python using the Model Context Protocol. Uses pluggable search backends…
2025 · github.com
- 23MT
Recently I was trying to use an MCP server to pull data from a service, but hit a limitation: the MCP didn't expose the data I needed, even though the service's REST API supported it. So I wrote a quick CLI wrapper around the API. Worked great, except Claude Code had no structured way to know what my CLI does or how to call it. For `gh` or `curl` the model can learn from the extensive training data, but for a tool I just wrote, it was stabbing in the dark. MCP solves this discovery problem, but it does it by rebuilding tool interaction from scratch: server processes, JSON-RPC transport,…
Feb 2026 · github.com
- 24CS
Hi HN, I built *CodeDrift*, a CLI tool that detects bugs commonly introduced by AI coding assistants like Copilot, Cursor and ChatGPT. Over the last year I noticed that AI tools often generate code that compiles correctly, passes linting and looks reasonable in code review but still contains subtle issues. Some common examples I kept seeing: * async `forEach` loops that never await promises * missing authorization checks (IDOR) * hallucinated dependencies that don’t exist * stack traces leaking sensitive information * request data used without validation These bugs often slip past ESLint,…
Mar 2026 · npmjs.com
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