"MobX-State-Tree" matching MCP servers:

Provides AI agents with powerful JavaScript/TypeScript code analysis and transformation capabilities using the tree-hugger-js library.

A Model Context Protocol (MCP) server that provides code analysis capabilities using tree-sitter, designed to give Claude intelligent access to codebases with appropriate context management.

MCP server for AI-assisted development on the Lampa open-source TV app, providing read-only access to the source tree with 41 tools and 5 resources for structured analysis, planning, editing, and validation.

Local MCP server for semantic code search using Tree-sitter AST parsing, local embeddings, and hybrid search; enables indexing and querying codebases entirely offline.

MCP server that analyzes codebases with tree-sitter and generates AGENTS.md files for AI agents.

MCP server that helps AI coding agents understand a repository by providing lightweight tree/map, code search, and token-budgeted context packing tools without dumping the entire monorepo into the prompt.

A deterministic structural code map server for AI agents, giving them the shape of a codebase (imports, exports, classes, functions, signatures, comments, TODO-markers) without reading whole files into context. Powered by tree-sitter WASM grammars, it runs anywhere Node 18+ works.

Deterministic navigation maps over code AND markdown for AI agents: one MCP server, two lenses (tree-sitter TS/JS/Python + markdown). Map a whole project, read one function or one doc-section, ~99% less context. Merges codelens + docslens.

Enables blazingly fast file and content searching in large codebases using ripgrep, with intelligent filtering, fuzzy finding, and directory tree visualization while respecting .gitignore and avoiding common bloat directories.

An MCP code-intelligence server for AI agents with pre-indexed AST cache, 62 MCP tools, and TOON-compressed output, enabling token-efficient code analysis and project health grading entirely locally.

A code-intelligence MCP server for coding agents that combines a static repository map (via tree-sitter) with precise live navigation (via LSP), providing structured, token-budgeted responses anchored to file:line.

Stop pasting your file tree into Claude. Give any AI assistant real architectural understanding of a codebase — local, private, zero‑config.

Enables AI assistants to search and analyze codebases using Abstract Syntax Tree (AST) pattern matching with ast-grep. Supports structural code search, pattern testing, and AST visualization across multiple programming languages.

Enables token-efficient structural code exploration using tree-sitter AST parsing, with tools for symbol search, outlines, source unfolding, call graphs, and related-file discovery.

Enables precise source code retrieval via tree-sitter AST parsing, cutting AI token costs by 86-99% by indexing codebases and fetching only needed symbols and structural queries (e.g., blast radius, importers) on demand.

Zero-config MCP server that connects local codebases to AI assistants, providing secure project tree, regex search, file reading, and tech stack tools locally.

Selects the minimum code context an LLM needs to understand a git diff: graph-based fragment selection under a token budget, deterministic output, 30+ tree-sitter languages.

MCP server for validating and linting Tonel formatted Smalltalk source code using tree-sitter-tonel-smalltalk

Enables LLM agents to navigate source code structurally via tree-sitter over MCP, offering outline-based reading, AST pattern search, workspace-scale symbol indexing, call graph analysis, and built-in security audit queries.

Serves graph-native code questions from a TypeScript codebase as an MCP server. It extracts structural graphs via tree-sitter and HydraDB to answer callers, impact, and context queries.