🎉 最新更新

** 📊 MCPMark 评估** - MCP-Universe 现在支持评估 MCPMark 任务

** MCP 🚀+** - MCP客户端上的代理封装器，可将令牌成本降低多达75%

** 🔬 深度研究代理**——通过并行工具调用扩展深度研究代理的宽度，提升性能和效率

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关于程序化网络研究，请参见深度研究工具（deep-research harness）快速入门。

什么是MCP-宇宙？

MCP-Universe 是一个全面的生态系统，用于构建、优化和评估与模型上下文协议（MCP）交互的 AI 代理。除了我们领先的实际 MCP 服务器交互基准外，MCP-Universe 还提供适用于代理开发的生产级工具，包括专用研究代理（Deep Research Agent）、智能上下文管理（MCP+**）以及复杂的编排工作流。

基准测试： 与依赖过于简单任务的现有基准不同，MCP-Universe通过与实际MCP服务器交互，在真实世界场景中评估LLMs，捕捉了实际应用挑战，解决了关键空白：

MCP-Universe

🎉 Latest Updates

📊 MCPMark Evaluation - MCP-Universe now supports evaluating the MCPMark tasks

🚀 MCP+ - Agentic wrapper on MCP clients which reduce token costs by up to 75%

🔬 Deep Research Agent - Scale the Width of Deep Research Agents with parallel tool calling, improving performance and efficiency

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For programmatic web research, see the deep-research harness quickstart.

What is MCP-Universe?

MCP-Universe is a comprehensive ecosystem for building, optimizing, and evaluating AI agents that interact with the Model Context Protocol (MCP). Beyond our industry-leading benchmark for real-world MCP server interactions, MCP-Universe provides production-ready tools for agent development including specialized research agents (Deep Research Agent), intelligent context management (MCP+), and sophisticated orchestration workflows.

Benchmarking: Unlike existing benchmarks that rely on overly simplistic tasks, MCP-Universe addresses critical gaps by evaluating LLMs in real-world scenarios through interaction with actual MCP servers, capturing real application challenges such as:

• 🎯 Long-horizon reasoning across multi-step tasks
• 🔧 Large, unfamiliar tool spaces with diverse MCP servers
• 🌍 Real-world data sources and live environments
• ⚡ Dynamic evaluation with time-sensitive ground truth

Table of Contents

• What’s New
• Architecture Overview
• Getting Started
  • Prerequisites
  • Installation
  • Quick Test
• Evaluating LLMs and Agents
  • Prerequisites
  • Environment Configuration
  • Benchmark Configuration
  • Execution
  • Save the running log