"A GitHub repository for a Minecraft server with many tools" 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).

Enables Claude Agent SDK agents to safely operate a remote user machine via a WebSocket tunnel, providing six client__* MCP tools with path fencing and phase-switch adjudication.

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.

An MCP server that provides a persistent sandbox for AI coding agents to explore codebases server-side, returning only compact summaries to reduce context consumption.

Enables MCP-capable AI agents to control a real Minecraft client through JsMacros scripting, screenshots, world markers, and player takeover, while the user watches and can stop or resume control.

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.

A meta-MCP server that orchestrates tools from multiple MCP servers, enabling complex Python workflows with loops and conditionals.

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.

A minimal MCP server that exposes a run_command tool over stdio, enabling command execution within a Docker container.

A dynamic MCP server that automatically discovers Python files in a directory and exposes them as tools to any MCP-compatible AI client, allowing users to easily create and deploy custom AI tools.

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.

Gives any MCP-compatible AI chat or agent a safe, model-neutral coding runtime with file read/search, structured multi-file patches, command execution, interactive sessions, and git operations, all confined to a single workspace and gated by permission modes.

Enables coding agents to perform file, search, patch, git, process, test, package, network, and system operations through 60 typed MCP tools with structured inputs/outputs, structured errors, and a full event journal, replacing terminal use with a typed machine API.

Empower any MCP-compatible AI Agent(MCP Client) with engineering-grade capabilities to understand, modify, run, and deliver real-world code repositories.

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.

Enables AI agents to execute tools securely with Docker isolation, tier-based permissions, and feature flags, providing a centralized tool registry and MCP server.

A Model Context Protocol (MCP) server that provides TypeScript development tools for automated refactoring and code analysis.

Extends VS Code Copilot with MCP tools for persistent semantic memory, code indexing and RAG search, sandboxed Python/shell execution, web fetch/search, dynamic tool management via a web UI, and structured multi-step reasoning.

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.

A collection of production-ready MCP tools for common tasks such as web search, code execution, file operations, and database queries, enabling AI agents to perform these actions through a unified interface.

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.

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.

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.

MCP server wrapping DeepSeek's agent harness tools into minimal/standard modes, enabling persistent shell, file editing, search, job management, and more via natural language.

Guides problem-solving by breaking down complex problems into steps and recommending appropriate MCP tools for each stage, with confidence scores and rationales for tool suggestions.

Enables dynamic creation and execution of custom tools/functions in multiple programming languages at runtime, exposing them to MCP clients like Claude.

A comprehensive MCP server providing tools for AI agents to interact with code, including reading symbols, importing modules, replacing text, and sending OS notifications.