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

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.

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 AI agents to maintain persistent, inspectable understanding through typed, revisable updates, and to coordinate multi-agent work via shared graph-based stigmergy.

Provides AI agents with local Dutch spelling and word validation using OpenTaal/hunspell, plus detailed word information from woordenlijst.org. It enables checking full texts or individual words and retrieving linguistic details for Dutch writing tasks.

This MCP server provides complete MediaWiki markup syntax documentation by dynamically fetching and consolidating information from official MediaWiki help pages. It enables LLMs to access up-to-date and comprehensive MediaWiki syntax information.

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.

Lets AI assistants act as a personal language teacher by storing a learner's vocabulary, writing stories around those words, judging translations, labeling mistakes by rule, and scheduling spaced-repetition reviews with FSRS. It also serves the agreed tutoring method to any connected assistant so teaching stays consistent across Claude, ChatGPT and Codex.

Enables AI agents to identify what language text is written in, returning top matches, confidence scores, script detection, and character statistics via a pay-per-call x402 API without requiring an API key.

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.

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

Enables local video inspection and offline transcription with timestamped JSON and Markdown output, enforced filesystem boundaries, and reproducible content-addressed caching.

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.

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

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.

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.

Exposes Language Server Protocol (LSP) tools such as diagnostics, goto definition, find references, symbols, and rename as a stdio MCP server.

Enables household members to use Alexa+ in their own language and script, with shared lists, reminders, devices, and messages stored as spoken and rendered for each person.

Provides AI agents with scoped access to backlog and task workflows, using PostgreSQL RLS tenant isolation and lease-based claims so they can safely read and update tasks within their own workspace.

Bridges the Model Context Protocol with Language Server Protocol to provide AI agents with persistent access to code intelligence features including navigation, diagnostics, refactoring, and completion across 7+ programming languages.

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.

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.

MCP server providing 29 tools across 5 layers for semantic TypeScript/JavaScript code intelligence, enabling AI agents to find references, trace impacts, guard APIs, and explain errors without text-search false positives.

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.

AST-Indexer is a Model Context Protocol (MCP) server for indexing Git repositories with AST parsing. It extracts functions, classes, imports, variables, and SQL schema objects from JavaScript, TypeScript, C#, and SQL codebases so they can be queried through MCP tools.

Enables AI agents to query language intelligence over MCP, including definitions, references, symbols, and code relationship graphs across many languages, all in one native process with no external language servers.

Enables AI assistants to interact with VS Code's language server protocol features (definitions, references, diagnostics) and debugger (breakpoints, stepping, variable inspection) via an MCP server.