"Understanding Perl, Language Server Protocol (LSP), and Abstract Syntax Trees (AST)" matching MCP servers:

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.

A smart proxy server that prevents context window bloat by caching large tool responses to local files and returning compact resource links. It supports format conversion and data uploads, allowing LLMs to handle massive datasets without consuming excessive tokens.

Enables code analysis of JavaScript, TypeScript, TSX, Python, and Rust files using Tree-sitter, providing symbol listing, definition/reference lookup, AST queries, LSP-style features, and SQLite indexing for efficient cross-file searches.

Provides advanced code structure and semantic analysis through Abstract Syntax Trees (AST) and Abstract Semantic Graphs (ASG) across multiple programming languages. It enables tasks like incremental parsing, complexity analysis, and AST diffing to help models understand and navigate codebases.

A zero-dependency Node.js playground for debugging how MCP clients authenticate to an MCP server over HTTP.

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.

This MCP server enables AI models to analyze local Python codebases using abstract syntax trees, providing tools for file structure analysis, symbol search, import graphing, docstring auditing, and refactoring prompts without loading entire source files into context.

Enables exploration and organization of research ideas using a hierarchical tile-based method with support for creating interconnected nodes (questions, hypotheses, methods, results), analyzing research gaps, and exporting to multiple visualization formats.

Enables agents to connect to Language Server Protocol servers as a headless editor, exposing LSP capabilities like diagnostics, rename, and formatting through MCP tools and resources.

An MCP server that exposes LSP-backed code navigation and editing tools to LLM agents using a single global config file to route file extensions to language servers.

Provides any MCP-capable coding agent with shared, warm language-server intelligence and project-local configuration, so diagnostics, definitions, references, hover, symbols, code actions, formatting, rename, and sync behave consistently across harnesses and sessions. This lets users switch agent tools without reconfiguring LSP integrations or falling back to CLI verification.

Enables AI code review to query semantic LSP context such as hover and definition against a specific Git commit or candidate, with verifiable receipts for the exact code state under review.

Provides AI agents with language-aware code analysis through the Language Server Protocol, enabling tasks like getting code insights and diagnostics.

This server provides verifiable code facts and entrypoint maps for a repository. It enables AI agents to understand the deterministic impact of code changes by providing evidence-based analysis of dependencies and usage.

Runs a language server and provides tools for communicating with it. Language servers excel at tasks that LLMs often struggle with, such as precisely understanding types, understanding relationships, and providing accurate symbol references.

Enables LLM agents to explore JavaScript/TypeScript repositories through parsed syntax trees by returning compact file outlines, precise symbol definitions, and local dependency graphs. It keeps access read-only and sandboxed to chosen roots while using a bounded, flood-proof stdio transport.

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.

Validates behavior trees for game AI and robotics, converts them between behavior3 JSON and BehaviorTree.CPP XML (Groot2, ROS 2 Nav2), and returns links that open them in the behaviortrees.com visual editor. Also provides built-in node docs, example trees and guides.

Enables AI assistants to query and analyze Rell code via Language Server Protocol features like hover information, completions, diagnostics, and code actions. Automatically manages the Rell LSP server and provides resource-based access with real-time subscriptions.

Enables AI coding agents to efficiently explore codebases by providing structural outlines, module digests, symbol bodies, and AST-aware grep via MCP.

Provides deep structural analysis of Python source code via AST, including function signatures, call graphs, cyclomatic complexity, code smells, dead code detection, and protocol conformance.

MCP server that keeps language server sessions warm and routes multiple languages through one process. Agents get persistent cross-file awareness, speculative execution (simulate edits before writing to disk), and 20 skills that encode correct multi-step operations like safe rename, blast-radius analysis, and end-to-end refactoring. Single Go binary, no runtime dependencies.

Bridges Claude Code to Language Server Protocol (LSP) servers to enable semantic code intelligence features like navigation, refactoring, and real-time diagnostics. It supports multiple languages including TypeScript, Python, and Rust with multi-root workspace capabilities.

Minimal MCP server bridging a Roslyn C# language server to AI agents, exposing tools for diagnostics, call hierarchy, and type hierarchy.

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.

Enables AI coding agents to interact with TypeScript projects through compiler-level code intelligence, providing tools for navigation, type information, diagnostics, refactoring, and semantic search.

Connects AI coding agents in the Antigravity CLI to native language servers for C#, C/C++, TypeScript/JavaScript, and Python through MCP tools, enabling precise symbol navigation, diagnostics, references, refactoring previews, and other code intelligence.

Provides code refactoring capabilities for TypeScript/JavaScript and Python through Language Server Protocol integration. Enables renaming symbols, extracting functions, finding references, and moving code between files via natural language commands.

Enables structural code search and refactoring across 20+ programming languages using ast-grep patterns, with safe dry-run mode and flexible filtering.

MCP server for agentic interaction with the Lean theorem prover via LSP, providing tools for understanding, analyzing, and interacting with Lean projects.