"A GitHub repository for a RAG documentation scraper" matching MCP servers:

Provides agents with real Linux VMs to run commands, manage files, expose ports, and track costs, all through an ephemeral sandbox service.

Enables AI models to access GitHub repository contents as context, with features to fetch entire repositories, specific file contents, and repository structures for use in AI interactions.

Anthropic's github MCP server, but better. Support for more endpoints. Including releases and tags, pull request reviews, statuses, rate limit, gists, projects, packages, and even pull request diffs. Indented to be used with MissionSquad's MCP API for secret management (aka your access token).

A read-only remote MCP gateway that lets MCP-capable agents discover GitHub REST endpoints and run many read-only GitHub reads (files, issues, comments, pull requests, checks) inside a single sandboxed JavaScript execution, returning only the compact result needed. This cuts model/tool round trips and context use, with enforced read-only access, repository allowlisting, and GitHub App authentication.

Implements a RAG workflow that integrates with any custom knowledge base and can be triggered directly from the Cursor IDE.

Enables agents to accomplish multi-step work by writing a single sandboxed JavaScript program that drives shell, filesystem, HTTP, and every tool of any configured MCP server, returning only a small summary instead of raw JSON. This collapses dozens of advertised tool schemas into one tool, cutting per-request context by roughly 26× while keeping disabled upstream servers reachable.

Enables local AI agents to manage GitHub Codespaces workspaces and execute remote tasks by checking out exact commits, running builds, tests, scripts or temporary services, downloading logs and artifacts, and stopping or exporting managed environments.

Enables AI agents to invoke a local Stata installation, including a Windows Stata build under Wine on Linux and macOS, for regression analysis, econometrics, paper replication, and empirical research with command guards, resource monitoring, and automatic log capture.

Enables Large Language Models to access and manipulate Autodesk Revit models through a pyRevit-based bridge and the Model Context Protocol.

Gives AI coding agents typed, mode-gated access to a real WordPress site — content, media, users and settings via core REST, plus PHP execution, SQL queries, filesystem operations and Elementor page read/build/patch/snapshot via a companion bridge plugin. Destructive actions and code execution are guarded by feature flags, explicit confirm literals, dry runs, redaction and audit logging.

Provides a secure workspace for AI-driven file management and Python script execution designed to run on Vercel. It enables tools for full file operations and code execution within a sandboxed environment.

Gives LLM agents spatial awareness inside Autodesk Maya, enabling scene inspection and measurement, aesthetic and engineering audits, camera planning, checkpoint/rollback safety, asset import, and viewport or render previews. Agents can also execute arbitrary Python in Maya to build and verify 3D scenes against engineering rules instead of writing blind.

A Model Context Protocol server that provides tools for interacting with both Radicle (peer-to-peer code collaboration) and GitHub through a unified interface, enabling repository management, synchronization, and collaboration features across both platforms.

A simple MCP server implementation in TypeScript that communicates over stdio, allowing users to ask questions that end with 'yes or no' to trigger the MCP tool in Cursor.

Agentic RAG server for document management, hybrid search, question answering with citations, and analytical code execution, powered by LanceDB and Pydantic AI.

Enable Claude (or any other LLM) to interactively debug your code (set breakpoints and evaluate expressions in stack frame). It's language-agnostic, assuming debugger console support and valid launch.json for debugging in VSCode.

A self-hosted MCP server that enables AI coding agents to read, edit, search, and run code in local projects with human review loops and policy controls.

A Model Context Protocol server that enables AI models to access tools through GitHub authentication and provides comprehensive analytics tracking of tool usage, performance, and user behavior.

MCP server that gives AI agents isolated, named Linux computers with stable IDs and lifecycle control. Supports multiple backends like Docker, Fly Machines, Modal, and E2B, with a dashboard for managing sandboxes.

A Python server implementing the Model Context Protocol to provide customizable prompt templates, resources, and tools that enhance LLM interactions in the continue.dev environment.