Holy Bio MCP——将你所有喜爱的智能生物工具汇聚在同一框架下
概览
Holy Bio MCP 是一个统一框架，诞生于2025年Bio x AI黑客松期间，基于我们在 、 和 的实际构建代理的经验。我们的目标是通过提供一套专门且互操作的工具，让研究人员的工作更轻松，这些工具能够无缝集成到AI驱动的集成开发环境（如Cursor、Windsurf）、聊天客户端（如Anthropic的Claude Desktop），并作为复杂代理工作流程（使用ElizaOS、Just-Agents、AutoGen等）的基础构建模块。 该项目将多个强大的独立MCP服务器聚集成一个统一的生态系统，用于先进的生物信息学和长寿命研究，构建自主研究代理。 MCP是一种协议，旨在弥合大型语言模型（LLM）与专门领域工具和数据库之间的鸿沟。它为AI助手和代理通过结构化、类型安全接口访问复杂功能提供了标准化方式。
概述（AI 翻译）
Developed 2025年生物×人工智能黑客马拉松期间
🎯 我们的使命
Holy Bio MCP 是一个统一框架，诞生于2025年Bio x AI黑客马拉松期间，源自我们在Longevity Genie项目、IBIMA（医学与衰老研究生物统计与信息学研究所）以及衰老系统生物学组中构建LLM驱动代理的实践经验。我们的目标是通过提供一套专门且互操作的工具，让研究人员的工作更轻松，这些工具能够无缝集成到AI驱动的IDE（如Cursor、Windsurf）、聊天客户端（如Anthropic的Claude Desktop），并作为复杂代理工作流程（使用ElizaOS、Just-Agents、AutoGen等）的基础构建模块。
该项目将多个强大的独立MCP服务器聚集成一个统一的生态系统，用于高级生物信息学和长寿命研究，包括原生ElizaOS插件用于构建自主研究代理。
什么是模型上下文协议（MCP）？
MCP是一种协议，旨在弥合大型语言模型（LLM）与专门领域工具和数据库之间的差距。
本页由 AI 翻译，技术细节请以原文为准。
README
以下为原文 README 节选，完整内容请查看原文。
Holy Bio MCP: A Unified MCP Framework for Bioinformatics Research
Developed during Bio x AI Hackathon 2025
🎯 Our Mission
Holy Bio MCP is a unified framework born during Bio x AI Hackathon-2025 from our practical experience building LLM-powered agents at the Longevity Genie project, IBIMA (Institute for Biostatistics and Informatics in Medicine and Ageing Research), and the Systems Biology of Aging Group. Our goal is to make life easier for researchers by providing a suite of specialized, interoperable tools that seamlessly integrate into AI-driven IDEs (like Cursor, Windsurf), chat clients (like Anthropic’s Claude Desktop), and serve as foundational building blocks for complex agentic workflows (using ElizaOS, Just-Agents, AutoGen, etc.).
This project aggregates several powerful, standalone MCP servers into a single, cohesive ecosystem for advanced bioinformatics and longevity research, including a native ElizaOS plugin for building autonomous research agents.
What is the Model Context Protocol (MCP)?
MCP is a protocol designed to bridge the gap between Large Language Models (LLMs) and specialized, domain-specific tools and databases. It provides a standardized way for AI assistants and agents to access complex functionalities through structured, type-safe interfaces.
If you want to understand MCP in more detail and learn how to use it effectively, we recommend these resources:
- DeepLearning.AI Course: MCP: Build Rich-Context AI Apps
- Official Documentation: Model Context Protocol Website
- Community: Search for “Model Context Protocol” on YouTube and other platforms for tutorials and discussions.
🔬 Real-World Example in Action
Here’s a real conversation (that you can easily reproduce yourself as all MCP-es are working and covered with tests) showing Holy Bio MCP servers working together to answer complex research questions, demonstrating the power of this integrated approach:
🎬 Video Demo
Here is a video demo of Holy-Bio-MCP, this time including synergistic genetic interventions and small molecules:
安装
uvx --from biothings-mcp stdio配置
{
"mcpServers": {
"biothings-mcp": {
"command": "uvx",
"args": ["--from", "biothings-mcp", "stdio"]
},
"gget-mcp": {
"command": "uvx",
"args": ["gget-mcp", "stdio"]
},
"synergy-age-mcp": {
"command": "uvx",
"args": ["synergy-age-mcp"],
"env": {
"MCP_TRANSPORT": "stdio"
}
},
"opengenes-mcp": {
"command": "uvx",
"args": ["opengenes-mcp"],
"env": {
"MCP_TRANSPORT": "stdio"
}
},
"pharmacology-mcp": {
"command": "uvx",
"args": ["--from", "pharmacology-mcp", "stdio"]
}
}
}