"Using Command Line with MCP (Master Control Program)" matching MCP servers:

A secure Model Context Protocol server that allows AI models to safely interact with Windows command-line functionality, enabling controlled execution of system commands, project creation, and system information retrieval.

MCP server for managing Markdown-Driven Task Management (MDTM) files with task, parent task, environment, and workflow management, enabling structured development workflows.

Provides a structured map of 120 practical AI prompts across 6 business domains, enabling users to quickly generate complete prompts for specific tasks using slash commands or tools.

A local knowledge graph MCP server that provides AI agents with permanent, structured memory about codebases, enabling semantic search, blast radius analysis, and convention enforcement.

Middleware that converts a serverless handler with OpenAPI spec into an MCP endpoint following stateless MCP principles.

This repository provides a template for creating MCP servers with Python, including installation instructions and tools for testing with the MCP Inspector.

Connects AI coding agents to the SkillsMP marketplace, allowing users to search, read, and install over 8,000 community-made skills. It enables agents to gain new capabilities either through on-the-spot instruction or permanent installation without requiring an API key.

Generates and manages slash commands for AI coding assistants like Claude Code and Cursor, supporting both interactive CLI and programmatic MCP access.

mcp-test-runner is an MCP server that lets your AI client (Claude / Cursor / Codex / Gemini) drive your entire QA loop end-to-end: * Run tests across pytest / Jest / Cypress / Go / Maestro — single MCP surface, one env var to switch * Analyze a URL (Web DOM probe) or a live mobile screen (Maestro hierarchy) to extract testable modules + candidate cases * Generate runna

Automatically analyzes project dependencies to discover and download the most relevant documentation, enabling developers to quickly set up comprehensive project context.

MCP server that enables AI agents to read, edit, and create PLC projects in EcoStruxure Control Expert via COM automation.

Portable governance control-promotion CLI and MCP server.

Enables AI agents to inspect Figma selections, navigate pages, render previews, generate starter code, and submit user-approved canvas edits through a local bridge.

Transforms Claude into a GSAP animation expert with AI-powered natural language animation creation, complete API coverage, and production-ready patterns for all GSAP features and plugins.

Enables one coding agent to inspect, configure, debug, and repair another agent over stdio MCP, with redacted reads and confirm-gated writes and restarts.

Multi-stack scaffolding engine for generating production-ready module structures (NestJS, Java Spring, Python FastAPI) directly from an MCP-compatible AI client, reducing token usage for boilerplate code.

Aggregates multiple MCP servers behind a single, secure endpoint with unified tool/resource discovery, OAuth authentication, and resilient request routing. Enables users to manage and interact with multiple MCP backends through one centralized interface with load balancing and circuit breakers.

An advanced MCP server for browser automation using Puppeteer, specifically optimized for token efficiency through minimal data returns and progressive enhancement. It enables agents to navigate pages, capture LLM-optimized screenshots, extract structured content, and perform batch interactions.

A production-ready MCP server for triaging and reviewing GitHub pull requests via the GitHub REST API, providing typed tools to list, inspect, comment, add labels, and submit reviews.

Enables safe, structured control of Blender on Windows via a standalone STDIO MCP server, providing seven tools to inspect scenes, list and edit materials, transform objects, add modifiers, set smooth shading, and export assets without arbitrary code execution.