"Collaboration Between Multiple AIs via MCP (Master Control Program)" matching MCP servers:

A local-first MCP server that gives AI coding agents runtime visibility and AI-managed debug logging. It replaces blind print() debugging by turning runtime execution into causal chains, allowing agents to instantly locate bugs by finding missing .success events in Python and TypeScript code. Single binary with MCP, CLI, and HTTP interfaces.

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.

Enables Claude to collaborate with Gemini for code reviews, second opinions, and iterative software development. It facilitates multi-step workflows including PRD creation and code generation through an AI orchestration framework.

Enables collaboration with multiple AI providers (Claude, GPT-4, Gemini, Ollama) directly from VS Code with automatic project context injection and persistent conversation history. Provides streamlined tools for getting AI advice, multi-provider research, and enhanced context sharing across sessions.

Enables AI agents to interact with collaborative Jupyter notebooks and documents in real-time, supporting notebook operations, document editing, user awareness tracking, and session management through Jupyter's RTC capabilities.

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.

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.

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

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.

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.

Provides a framework for running multiple MCP servers using FastAPI, stdio, and streamable-http transports, with an echo tool for testing and integration with MCP Inspector.

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.

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

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.

Enables AI assistants to explore and interact with the Facets Control Plane API via natural language, supporting operations like searching endpoints, loading schemas, and making authenticated API calls.

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.

The control plane for AI prompts that scores, enforces policy, locks config, and audits every prompt decision.