Browse and discover Model Context Protocol compatible servers
This repository hosts a sleek AI assistant and MCP client that supports various AI models like OpenAI, Google, and Mistral, and offers tools requiring Python, Node.js, and uv for activation.
HttpRunner 是一款开源的 API/UI 测试框架，简单易用，功能强大，具有丰富的插件化机制和高度的可扩展能力。
Desktop Commander MCP enables Claude to execute terminal commands, manage processes, and perform file system operations, including search and replace, through the Model Context Protocol. It supports long-running commands and session management.
The Firecrawl MCP Server integrates with Firecrawl for web scraping, offering features like URL discovery, content extraction, and batch processing with rate limiting. It supports cloud and self-hosted instances, along
⚡️AI Cloud OS: Open-source enterprise-level AI knowledge base and MCP (model-context-protocol)/A2A (agent-to-agent) management platform with admin UI, user management and Single-Sign-On⚡️, supports Ch
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**Option 1 (Focus on integration):** Open Source MCP integration library for AI apps. (Klavis AI) **Option 2 (Focus on context):** Klavis AI's Open Source MCP: Context management for AI application
RuoYi AI 是一个全栈式 AI 开发平台，旨在帮助开发者快速构建和部署个性化的 AI 应用。
This repository provides a curated list of awesome Model Context Protocol (MCP) servers, which are production-ready and experimental implementations that extend AI capabilities through various contextual services. This repository provides
OpenSumi is a framework designed to help you quickly build AI Native IDE products. It offers tools and components for creating customized IDEs, supported by CI/CD, testing, and community engagement features.
This repository provides learning materials for the Panaversity Certified Agentic & Robotic AI Engineer program, covering AI-201 and AI-202 courses focused on building conversational and autonomous AI agents.
Self-Hosted Plaform for Secure Execution of Untrusted User/AI Code
**Concise Description:** Deploy agents, models, RAG & pipelines easily. MCP server simplifies AI deployment. No YAML/MLOps needed.