"Using a TypeScript codebase as context for a tool or service" matching MCP servers:

MCP server connecting Claude/Cursor to Codex CLI, enabling code analysis via @ file references, multi-turn conversations, sandboxed edits, and structured change mode.

An MCP server that connects AI assistants to a Neo4j graph database, enabling querying of movie data through tools like graph statistics, genre-based search, and movie details, along with advanced features such as LLM sampling and completions.

Unified MCP orchestration layer that consolidates multiple MCPs into a single interface with semantic tool discovery, code-mode execution, scheduling, and intelligent caching to reduce token usage by 97% and eliminate choice paralysis.

Provides comprehensive filesystem operations, text search/replace, image processing, and Tesseract OCR capabilities for AI agents via the Model Context Protocol.

A TypeScript-based server that enables programmatic management of pseudo-terminal sessions. It allows users to spawn, control, and interact with terminal processes in real-time through the Model Context Protocol.

Fetches YouTube video transcripts, using free captions when available and falling back to Gemini-based transcription for missing or broken captions.

The MCP server for Nanostores that scans your codebase with AST analysis to find stores, subscribers, and dependency chains, and can ingest live events to spot noisy, error-prone, or unused stores.

Enables fast literal and regex search over local workspaces using a sparse n-gram index for candidate pruning, with deterministic verification against file contents.

Enables MCP clients to search Walmart products by keyword or category, retrieve product details including the buy box and other sellers, and page through customer reviews with filters, all as structured JSON without a Walmart developer account.

Enables MCP clients to send prompts anonymously to ChatGPT and receive the markdown answer plus the web sources it consulted as structured JSON. Requires only a HasData API key, with no OpenAI account, API key, or billing needed.

A simple MCP server that provides a basic greeting tool and serves as a starter template for AWS Lambda deployment. Demonstrates how to build and deploy MCP servers with both local development and cloud deployment capabilities.

Single-binary Go MCP server that wraps whatsmeow to expose a personal WhatsApp account as 41 MCP tools (messaging, groups, polls, media, privacy).

Operations toolchain for SQLite, Cloudflare D1, and Turso. Every command is available as both a CLI and an MCP tool: inspect, diff, migrate, monitor, back up, and repair databases. 22 stdio tools, read-only by default.

Enables AI agents to submit software tools to the Altimateguide directory for editorial review through a single submit_tool operation. Submissions are queued as pending and never published automatically.

A production-grade TypeScript starter for building Model Context Protocol servers, supporting stdio and Streamable HTTP transports with modular tools, resources, and prompts.

A smart AniList integration for the Model Context Protocol that provides AI assistants with tools for searching media, managing watchlists, and analyzing user anime or manga tastes. It goes beyond basic API calls by offering personalized recommendations, taste comparisons, and natural language profile summaries.

Lets any MCP client turn a plain-language trip description into dated fares from a single origin airport, returning prices alongside typical route pricing, airlines, stops, durations and booking links as structured JSON. Optional filters for destination, cabin, dates, trip length and price or stop ceilings are available once a destination airport is specified.

DevEyes is a Model Context Protocol (MCP) server that captures screenshots from your local development environment and automatically optimizes them for LLM consumption.

A communication framework that enables multiple AI agents to collaborate through asynchronous messaging, agent registration, and discovery. It facilitates complex task coordination and real-time discussion across distributed agent workflows using the Model Context Protocol.

A Model Context Protocol server that exposes a MeshCore node and the mesh reachable through it as a clean, high-signal interface for AI agents or tools.