"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.

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.

Saves the user's exact words as immutable, SHA-256-hashed captures before anything else, then derives events, entities, context cards, and causal hypotheses — every fact traceable to its verbatim source. Local-first personal memory for Claude Code, Codex, and any MCP client.

Integrates Xiaomi MiMo's multimodal API to enable understanding of images, audio, and video through natural language prompts.

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.

Provides JavaScript/TypeScript code intelligence via LSP, enabling goto definition, hover info, and find references through MCP tools.

An MCP server for interacting with the Rell Language Server Protocol interface. It enables LLMs to query LSP Hover and Completion providers for Rell projects.

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.

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.

Autonomous DeFi yield for AI agents on Base. Query APY rates, agent status, payment splits, and referral stats. 4 read-only tools + 1 resource.

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.

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

An MCP server that analyzes local or remote GitHub repositories, providing intelligent code context and structure to AI coding assistants.

Provides deterministic Python codebase symbol exploration using AST parsing, with tools for definition lookup, signatures, references, call chains, and class hierarchy queries.

Provides CodeQL language intelligence to AI agents via MCP, enabling completions, hover, definitions, references, diagnostics, formatting, and in-memory file updates for QL code.

Enables building and evaluating LangGraph agents over a protocol-level MCP server to measure failure rates across four agent topologies.

Trust infrastructure for the agent economy.

Enables LLM agents to interact with the Lean theorem prover through the Language Server Protocol, providing tools for analyzing Lean projects, accessing diagnostics, goal states, documentation, and searching for theorems using both local and external search services.