"Understanding Abstract Syntax Trees (AST)" matching MCP servers:

AST-targeted code editing MCP server with 28 surgical tools for structural edits across 11 languages. Built on tree-sitter, replaces brittle search/replace with byte-correct edits keyed by symbol names.

Combines AST intelligence and guarded file operations to provide a secure, controlled repository workflow for coding agents, enabling structural code analysis and safe edits without unbounded editor access.

A robust, language-agnostic Model Context Protocol (MCP) server that provides AI coding agents with the ability to edit files surgically via Abstract Syntax Trees (AST) instead of relying on token-heavy, brittle search-and-replace or diff operations.

ast-mcp-server gives coding agents compact, type-aware access to TypeScript and JavaScript projects, providing structural code tools based on the real compiler project model through ts-morph for declarations, references, renames, and diagnostics, with safe prepare → review → apply mutation semantics and immutable hash-bound plans.

Turn any MCP-aware IDE into an agentic coding system backed by tree-sitter, a local Qwen 18B, OpenRouter, and Podman.

Enables structured, step-by-step problem-solving through dynamic thinking processes that can be revised, branched, and adjusted as understanding deepens. Supports breaking down complex problems into manageable steps with the ability to revise previous thoughts and explore alternative reasoning paths.

Provides TypeScript and JavaScript code refactoring operations using ts-morph, allowing AST-based symbol renaming, file/folder renaming, reference searching, and path alias removal when integrated with editor extensions like Cursor.

Provides AI coding agents with five intelligence layers (dependency graph, git history, documentation, architectural decisions, code health) via nine MCP tools, enabling deep codebase understanding and reducing exploration cost.

A local AST and type-aware Go repository context indexer that uses CHA to map symbols, call graphs, dependencies, and architecture patterns. Built for AI coding agents that need to navigate and reason about large Go codebases without reading raw files.

MCP server that analyzes videos locally with frame-by-frame understanding and optional browser sidecars, producing structured context bundles for coding agents to fix bugs, add features, or build new functionality.

MCP server that provides AI coding agents automatic access to AGENTS.md documentation from GitHub repositories, enabling understanding of codebase conventions and patterns.

Persistent daemon that compresses codebases via AST body folding before indexing them for AI coding assistants like Claude Code and Cursor 57-89% token reduction with sub-millisecond queries, verified on Django, Spring, and Next.js.

A Model Context Protocol (MCP) server providing 62 AI-optimized tools for .NET/C# semantic code analysis, navigation, refactoring, and code generation using Microsoft Roslyn. Built for AI coding agents - provides compiler-accurate code understanding that AI cannot infer from reading source files alone.

Provides IDE-like semantic code retrieval and editing tools for LLMs, enabling precise code understanding and manipulation in large codebases via the Model Context Protocol.

An MCP server that automates code review by providing syntax checking, code explanation, and improvement suggestions using a multi-agent system.

An MCP server leveraging the Rust-based ty type checker to provide AI models with high-performance, semantic Python code analysis and structural navigation. It enables precise symbol searching, cross-file renaming, and diagnostic reporting to improve code understanding and editing accuracy.

An MCP server that enables Claude and other LLM clients to access Hanzo AI platform capabilities and provide powerful code management tools for analyzing, modifying, and understanding projects.

Architectural Gatekeeper for AI coding. Prevents "tunnel vision" bugs by forcing the AI to verify dependencies (via AST parsing) before editing files. Supports JavaScript & TypeScript. Blocks unsafe edits until the AI proves it understands the impact

Local-first code intelligence and safety layer for AI coding agents. MCP server exposes dependency graph, impact analysis, and AST-compressed repo context, backed by typed local memory, patch-scope safety gates, and git-independent transaction rollback.

Enables coding agents to check human-stated assumptions against authoritative sources like AWS AppConfig, Sentry, and the repository, detecting when beliefs have drifted from reality. It provides tools to inspect belief history, list verifiable properties, review open drifts, and record verified understanding.