Chiasmus is an MCP server that gives language models access to formal verification

🤖 A Model Context Protocol (MCP) server for Google Cloud (GCP)

A MCP (Model Context Protocol) server that provides tools for controlling and interacting with Android devices using uiautomator2.

Playwright AI Agent POM MCP ServerPlaywright AI Agent using Page Object Model (POM) architecture with MCP Server integration for automated web and mobile testing

An MCP (Model Context Protocol) server that enables AI assistants to control iOS Simulator. Seamlessly integrates with Claude Desktop, Cursor, Claude Code, and other MCP-compatible clients.

Laravel Loop is a powerful Model Context Protocol (MCP) server designed specifically for Laravel applications. It connects your Laravel application with AI assistants using the MCP protocol.

📦 Repomix is a powerful tool that packs your entire repository into a single, AI-friendly file. Perfect for when you need to feed your codebase to Large Language Models (LLMs) or other AI tools like Claude, ChatGPT, DeepSeek, Perplexity, Gemini, Gemma, Llama, Grok, and more.

A comprehensive Model Context Protocol (MCP) server that enables AI assistants to control Unreal Engine through the native C++ Automation Bridge plugin. Built with TypeScript and C++.

Peekaboo is a macOS CLI & optional MCP server that enables AI agents to capture screenshots of applications, or the entire system, with optional visual question answering through local or remote AI models.

Code, Build and Evaluate agents - excellent Model and Skills/MCP/ACP/A2A Support

Agentic development harness for Claude Code — SPEC-driven plan/run/sync, TRUST 5 quality gates, model+effort routing, and Claude×GLM multi-LLM cost control. Single Go binary, 16 languages, zero deps.

A Model Context Protocol (MCP) server that provides seamless integration with Neovim instances, enabling AI assistants to interact with your editor through connections and access diagnostic information via structured resources.

A system monitoring tool that exposes system metrics via the Model Context Protocol (MCP). This tool allows LLMs to retrieve real-time system information through an MCP-compatible interface.

The Fable Workflow: how Claude Fable 5 worked, distilled into skills any model can run, with the eval that keeps it honest. Think / act / prove.

A Model Context Protocol (MCP) server for Langfuse, enabling AI agents to query Langfuse trace data for enhanced debugging and observability

An open-source plugin that runs inside Codex and lets you use Claude Code and Claude models for review, rescue, and tracked background workflows.

Plan-then-build AI coding for Claude Code & Codex CLI — you approve the plan before the model writes a line of code. SPEC → PLAN → TEST → CODE → REVIEW → LEARN

From text & real sources to maintainable .drawio architecture models: Diagram IR with source-kind profiles, incremental sync preserving manual layout, multi-view projection, architecture-as-test with a CI action, query/review, what-if, accessible Story Mode, and a built-in MCP server

A Whistle proxy management tool based on Model Context Protocol that allows AI assistants to directly control local Whistle proxy servers, simplifying network debugging, API testing, and proxy rule configuration through natural language interaction.

Agentic coding skills for backtesting trading strategies using VectorBT. Supports Indian, US, and Crypto markets with realistic transaction cost modeling, TA-Lib indicators, QuantStats tearsheets, and 12 ready-made strategy templates.

MCP server that gives any LLM its own computer — managed Docker workspaces with live browser, terminal, code execution, document skills, and autonomous sub-agents. Self-hosted, open-source, pluggable into any model.

Dynamic Shell Command MCP Server

Give your AI assistant superpowers for Zotero plugin development. 28 tools for screenshots, DOM inspection, JavaScript execution, build integration, and debugging via Model Context Protocol.

A codex plugin for running optimization loops inside a codebase. It is useful when you have a measurable target and many possible changes to try: test runtime, build speed, bundle size, model loss, Lighthouse scores, memory use, query latency, or any other metric you can print from a script.