🔬 AI Research MCP Server

实时追踪 AI/LLM 研究进展的 MCP 服务器

](https://www.python.org/downloads/) ![License](LICENSE) ![MCP](https://modelcontextprotocol.io)

English | 中文

📖 简介

一个基于 Model Context Protocol (MCP) 的智能服务器，帮助研究者和开发者实时追踪 AI/LLM 领域的最新进展。

🎯 核心功能

📚 多源集成 - arXiv、GitHub、Hugging Face、Papers with Code
🔍 智能搜索 - 按关键词、领域、时间范围搜索
📊 自动汇总 - 每日/每周研究进展自动生成
⚡ 高效缓存 - 智能缓存机制，减少 API 调用
🌍 覆盖全面 - 15+ AI 研究领域全覆盖

✨ 功能特点

📚 多数据源集成

arXiv - 搜索最新的 AI/ML 学术论文
Papers with Code - 获取带代码实现的热门论文
Hugging Face - 每日精选论文、热门模型和数据集
GitHub - 追踪高 star 的 AI 项目和 trending 仓库

🎯 覆盖的 AI 研究领域

核心 AI/ML: 大语言模型 (LLM)、Transformer、深度学习
多模态与生成: CLIP、Stable Diffusion、文本生成图像
机器人学: 具身智能、机械臂控制、导航
生物信息学: 蛋白质折叠、药物发现、基因组学
AI for Science: 科学计算、物理模拟
强化学习: 多智能体、策略梯度、离线 RL
图神经网络: 分子建模、知识图谱
高效 AI: 模型压缩、量化、LoRA
AI 安全: 对齐、可解释性、公平性
新兴方向: 联邦学习、持续学习、神经形态计算

🛠️ MCP 工具

search_latest_papers: 搜索最新 AI 论文
search_github_repos: 搜索热门 AI GitHub 仓库
get_daily_papers: 获取今日精选论文
get_trending_repos: 获取 GitHub trending 仓库
get_trending_models: 获取 Hugging Face 热门模型
search_by_area: 按研究领域搜索（LLM、视觉、机器人等）
generate_daily_summary: 生成每日 AI 研究汇总
generate_weekly_summary: 生成每周 AI 研究汇总

📊 MCP 资源

ai-research://daily-summary: 每日 AI 研究汇总（自动缓存）
ai-research://weekly-summary: 每周 AI 研究汇总（自动缓存）

🚀 快速开始

前置要求

Python 3.10+
pip 包管理器
Claude Desktop (推荐) 或其他 MCP 客户端

安装步骤

# 1. 克隆仓库
git clone https://github.com/nanyang12138/AI-Research-MCP.git
cd AI-Research-MCP

# 2. 安装依赖
pip install -e .

# 3. (可选) 配置 GitHub Token
cp .env.example .env
# 编辑 .env 文件，添加你的 GitHub Token

💡 提示: 查看 QUICKSTART.md 获取更详细的安装指南

⚙️ 配置

环境变量（可选）

创建 .env 文件：

# GitHub Personal Access Token (强烈推荐)
# 提高 API 速率限制: 60 req/h → 5000 req/h
GITHUB_TOKEN=ghp_xxxxxxxxxxxxxxxxxxxx

# 缓存目录（可选，默认 .cache）
CACHE_DIR=.cache

# 缓存过期时间（秒）
CACHE_EXPIRY_GITHUB=3600      # 1 小时
CACHE_EXPIRY_ARXIV=7200        # 2 小时
CACHE_EXPIRY_SUMMARY=86400     # 24 小时

🔑 获取 GitHub Token

虽然可选，但强烈推荐配置以避免 API 速率限制

点击展开配置步骤

访问
点击 Generate new token (classic)
勾选 public_repo 权限
复制生成的 token
添加到 .env 文件

GITHUB_TOKEN=ghp_your_token_here

💬 在 Claude Desktop 中使用

配置 Claude Desktop

编辑 Claude Desktop 配置文件：

操作系统	配置文件路径
macOS	`~/Library/Application Support/Claude/claude_desktop_config.json`
Windows	`%APPDATA%\Claude\claude_desktop_config.json`
Linux	`~/.config/Claude/claude_desktop_config.json`

方式 1: 使用 Python 命令（推荐）

{
  "mcpServers": {
    "ai-research": {
      "command": "python",
      "args": ["-m", "ai_research_mcp.server"],
      "env": {
        "GITHUB_TOKEN": "your_github_token_here"
      }
    }
  }
}

方式 2: 使用绝对路径

{
  "mcpServers": {
    "ai-research": {
      "command": "C:\\Users\\YourName\\path\\to\\python.exe",
      "args": ["-m", "ai_research_mcp.server"],
      "env": {
        "GITHUB_TOKEN": "your_github_token_here"
      }
    }
  }
}

重启 Claude Desktop

配置完成后，重启 Claude Desktop 以加载 MCP 服务器。

在聊天窗口右下角应该能看到 🔌 图标，表示 MCP 服务器已连接。

📖 使用示例

在 Claude Desktop 中，你可以这样提问：

🔍 搜索最新论文

帮我找最近一周关于大语言模型的论文

搜索最近三天关于多模态模型的研究

有什么关于 Diffusion Model 的新论文吗？

💻 查找 GitHub 仓库

有哪些新的高 star LLM 相关仓库？

找一些关于机器人学习的 GitHub 项目

最近有什么火热的 AI 开源项目？

📊 获取每日汇总

生成今天的 AI 研究汇总

给我看看本周的 AI 研究进展

今天有什么重要的 AI 新闻吗？

🎯 按领域搜索

帮我找生物信息学领域的最新 AI 研究

搜索强化学习的最新论文和项目

计算机视觉领域有什么新进展？

🤖 追踪模型

Hugging Face 上有哪些热门的新模型？

最近有哪些流行的文本生成模型？

有什么新发布的开源 LLM 吗？

💡 查看 EXAMPLES.md 获取更多使用示例

技术架构

项目结构

ai-research-mcp/
├── src/
│   └── ai_research_mcp/
│       ├── __init__.py
│       ├── server.py              # MCP 服务器主文件
│       ├── data_sources/          # 数据源客户端
│       │   ├── arxiv_client.py
│       │   ├── github_client.py
│       │   ├── huggingface_client.py
│       │   └── papers_with_code_client.py
│       └── utils/
│           └── cache.py           # 缓存管理
├── pyproject.toml
└── README.md

缓存机制

为了减少 API 调用次数和提高响应速度，服务器实现了文件缓存：

GitHub API 结果缓存 1 小时
arXiv 搜索结果缓存 2 小时
每日/每周汇总缓存 24 小时

缓存文件存储在 .cache 目录（可通过环境变量配置）。

API 数据源

arXiv

API: arXiv API
限制: 每 3 秒最多 1 个请求
覆盖类别: cs.AI, cs.CL, cs.LG, cs.CV, cs.RO, q-bio.*, 等

GitHub

API: GitHub REST API v3
限制:

- 无 token: 60 请求/小时 - 有 token: 5000 请求/小时

推荐: 配置 GitHub Token

Hugging Face

API: Hugging Face Hub API
限制: 较宽松，建议使用缓存
数据: 每日论文、模型、数据集

Papers with Code

API: Papers with Code API
限制: 较宽松
特点: 论文 + 代码实现

🔧 故障排除

❓ 为什么搜索结果为空？

可能原因：

关键词太具体 → 尝试使用更通用的术语
时间范围太短 → 增加 days 参数
API 速率限制 → 等待几分钟后重试
网络问题 → 检查网络连接

⚠️ GitHub API 速率限制错误

解决方法: 配置 GITHUB_TOKEN 环境变量

速率限制对比：

❌ 无 Token: 60 请求/小时
✅ 有 Token: 5000 请求/小时

🚫 服务器启动失败

检查清单：

[ ] Python 版本 >= 3.10
[ ] 依赖已安装: pip install -e .
[ ] 配置文件路径正确
[ ] 环境变量设置正确

🔄 缓存数据过时

删除缓存目录重新获取：

# Linux/macOS
rm -rf .cache

# Windows
rmdir /s .cache

👨‍💻 开发

运行测试

# 安装开发依赖
pip install -e ".[dev]"

# 运行测试
pytest

# 运行特定测试
python test_clients.py

代码格式化

# 格式化代码
black src/

# Lint 检查
ruff check src/

# 类型检查（可选）
mypy src/

🤝 贡献

我们欢迎任何形式的贡献！

如何贡献

Fork 本仓库
创建你的特性分支 (git checkout -b feature/AmazingFeature)
提交你的更改 (git commit -m 'Add some AmazingFeature')
推送到分支 (git push origin feature/AmazingFeature)
开启一个 Pull Request

贡献指南

遵循现有代码风格
添加适当的测试
更新相关文档
确保所有测试通过

📄 许可证

本项目采用 MIT 许可证 - 查看 LICENSE 文件了解详情

🙏 致谢

特别感谢以下项目和服务：

Anthropic MCP - Model Context Protocol
arXiv API - 学术论文数据
- 代码仓库数据
Hugging Face Hub - 模型和数据集
Papers with Code - 论文和代码配对

📝 更新日志

v0.1.0 (2025-10-28)

🎉 初始发布

✅ 集成 4 大数据源：arXiv、GitHub、Hugging Face、Papers with Code
✅ 实现 8 个 MCP 工具和 2 个 MCP 资源
✅ 智能缓存机制
✅ 覆盖 15+ AI 研究领域
✅ 完整的文档和示例

🗺️ 路线图

v0.2.0 (计划中)

[ ] 添加 OpenReview 和 SemanticScholar 集成
[ ] 支持自定义关键词订阅
[ ] 改进缓存策略和性能优化
[ ] 添加更多单元测试

v0.3.0 (未来)

[ ] Web 界面
[ ] 邮件通知功能
[ ] 导出为 PDF/HTML
[ ] 可视化图表

v1.0.0 (长期)

[ ] 多语言支持（完整中英文）
[ ] 智能推荐算法
[ ] 移动端支持

💬 社区

💡 提交功能建议
🐛 报告 Bug
💭 参与讨论
⭐ 如果觉得有用，请给我们一个 Star！

🌐 English Version

📖 Introduction

An intelligent server based on Model Context Protocol (MCP) that helps researchers and developers track the latest AI/LLM research progress in real-time.

🎯 Core Features

📚 Multi-source Integration - arXiv, GitHub, Hugging Face, Papers with Code
🔍 Smart Search - Search by keywords, domains, and time ranges
📊 Auto Summary - Automated daily/weekly research digest generation
⚡ Efficient Caching - Smart caching mechanism to reduce API calls
🌍 Comprehensive Coverage - 15+ AI research areas covered

✨ Features

📚 Multi-source Data Integration

arXiv - Search latest AI/ML academic papers
Papers with Code - Get popular papers with code implementations
Hugging Face - Daily featured papers, trending models and datasets
GitHub - Track high-star AI projects and trending repositories

🎯 Covered AI Research Areas

Core AI/ML: Large Language Models (LLM), Transformer, Deep Learning
Multimodal & Generation: CLIP, Stable Diffusion, Text-to-Image
Robotics: Embodied AI, Robot Arm Control, Navigation
Bioinformatics: Protein Folding, Drug Discovery, Genomics
AI for Science: Scientific Computing, Physics Simulation
Reinforcement Learning: Multi-agent, Policy Gradient, Offline RL
Graph Neural Networks: Molecular Modeling, Knowledge Graphs
Efficient AI: Model Compression, Quantization, LoRA
AI Safety: Alignment, Interpretability, Fairness
Emerging Directions: Federated Learning, Continual Learning, Neuromorphic Computing

🛠️ MCP Tools

search_latest_papers - Search latest AI papers
search_github_repos - Search trending AI GitHub repositories
get_daily_papers - Get today's featured papers
get_trending_repos - Get GitHub trending repositories
get_trending_models - Get Hugging Face trending models
search_by_area - Search by research area (LLM, Vision, Robotics, etc.)
generate_daily_summary - Generate daily AI research digest
generate_weekly_summary - Generate weekly AI research digest

📊 MCP Resources

ai-research://daily-summary - Daily AI research digest (auto-cached)
ai-research://weekly-summary - Weekly AI research digest (auto-cached)

🚀 Quick Start

Prerequisites

Python 3.10+
pip package manager
Claude Desktop (recommended) or other MCP clients

Installation Steps

# 1. Clone the repository
git clone https://github.com/nanyang12138/AI-Research-MCP.git
cd AI-Research-MCP

# 2. Install dependencies
pip install -e .

# 3. (Optional) Configure GitHub Token
cp .env.example .env
# Edit .env file and add your GitHub Token

💡 Tip: See QUICKSTART.md for detailed installation guide

⚙️ Configuration

Environment Variables (Optional)

Create a .env file:

# GitHub Personal Access Token (Highly Recommended)
# Increase API rate limit: 60 req/h → 5000 req/h
GITHUB_TOKEN=ghp_xxxxxxxxxxxxxxxxxxxx

# Cache directory (optional, defaults to .cache)
CACHE_DIR=.cache

# Cache expiry times (in seconds)
CACHE_EXPIRY_GITHUB=3600      # 1 hour
CACHE_EXPIRY_ARXIV=7200        # 2 hours
CACHE_EXPIRY_SUMMARY=86400     # 24 hours

🔑 Getting GitHub Token

Although optional, highly recommended to avoid API rate limits

Click to expand setup steps

Visit
Click Generate new token (classic)
Select public_repo permission
Copy the generated token
Add to .env file

GITHUB_TOKEN=ghp_your_token_here

💬 Using with Claude Desktop

Configure Claude Desktop

Edit Claude Desktop configuration file:

OS	Configuration File Path
macOS	`~/Library/Application Support/Claude/claude_desktop_config.json`
Windows	`%APPDATA%\Claude\claude_desktop_config.json`
Linux	`~/.config/Claude/claude_desktop_config.json`

Method 1: Using Python Command (Recommended)

{
  "mcpServers": {
    "ai-research": {
      "command": "python",
      "args": ["-m", "ai_research_mcp.server"],
      "env": {
        "GITHUB_TOKEN": "your_github_token_here"
      }
    }
  }
}

Method 2: Using Absolute Path

{
  "mcpServers": {
    "ai-research": {
      "command": "C:\\Users\\YourName\\path\\to\\python.exe",
      "args": ["-m", "ai_research_mcp.server"],
      "env": {
        "GITHUB_TOKEN": "your_github_token_here"
      }
    }
  }
}

Restart Claude Desktop

After configuration, restart Claude Desktop to load the MCP server.

You should see a 🔌 icon in the bottom right corner of the chat window, indicating the MCP server is connected.

📖 Usage Examples

In Claude Desktop, you can ask questions like:

🔍 Search Latest Papers

Find me papers about large language models from the past week

Search for recent research on multimodal models from the last 3 days

Any new papers on Diffusion Models?

💻 Find GitHub Repositories

What are some new high-star LLM related repositories?

Find some GitHub projects about robot learning

What are the trending AI open source projects recently?

📊 Get Daily Digest

Generate today's AI research digest

Show me this week's AI research progress

Any important AI news today?

🎯 Search by Domain

Find me the latest AI research in bioinformatics

Search for latest papers and projects in reinforcement learning

What's new in computer vision?

🤖 Track Models

What are the trending new models on Hugging Face?

Any popular text generation models recently?

Any newly released open-source LLMs?

💡 See EXAMPLES.md for more usage examples

🏗️ Technical Architecture

Project Structure

ai-research-mcp/
├── src/
│   └── ai_research_mcp/
│       ├── __init__.py
│       ├── server.py              # MCP server main file
│       ├── data_sources/          # Data source clients
│       │   ├── arxiv_client.py
│       │   ├── github_client.py
│       │   ├── huggingface_client.py
│       │   └── papers_with_code_client.py
│       └── utils/
│           └── cache.py           # Cache management
├── pyproject.toml
└── README.md

Caching Mechanism

To reduce API calls and improve response speed, the server implements file caching:

GitHub API results cached for 1 hour
arXiv search results cached for 2 hours
Daily/weekly digests cached for 24 hours

Cache files are stored in the .cache directory (configurable via environment variables).

🌐 API Data Sources

arXiv

API: arXiv API
Limits: Maximum 1 request per 3 seconds
Coverage: cs.AI, cs.CL, cs.LG, cs.CV, cs.RO, q-bio.*, etc.

GitHub

API: GitHub REST API v3
Limits:

- Without token: 60 requests/hour - With token: 5000 requests/hour

Recommendation: Configure GitHub Token

Hugging Face

API: Hugging Face Hub API
Limits: Relatively lenient, caching recommended
Data: Daily papers, models, datasets

Papers with Code

API: Papers with Code API
Limits: Relatively lenient
Features: Papers + code implementations

🔧 Troubleshooting

❓ Why are search results empty?

Possible reasons:

Keywords too specific → Try more general terms
Time range too short → Increase days parameter
API rate limit → Wait a few minutes and retry
Network issues → Check network connection

⚠️ GitHub API Rate Limit Error

Solution: Configure GITHUB_TOKEN environment variable

Rate limit comparison:

❌ Without Token: 60 requests/hour
✅ With Token: 5000 requests/hour

🚫 Server Startup Failed

Checklist:

[ ] Python version >= 3.10
[ ] Dependencies installed: pip install -e .
[ ] Configuration file path correct
[ ] Environment variables set correctly

🔄 Cached Data Outdated

Delete cache directory to refresh:

# Linux/macOS
rm -rf .cache

# Windows
rmdir /s .cache

👨‍💻 Development

Running Tests

# Install dev dependencies
pip install -e ".[dev]"

# Run tests
pytest

# Run specific tests
python test_clients.py

Code Formatting

# Format code
black src/

# Lint check
ruff check src/

# Type checking (optional)
mypy src/

🤝 Contributing

We welcome all forms of contributions!

How to Contribute

Fork this repository
Create your feature branch (git checkout -b feature/AmazingFeature)
Commit your changes (git commit -m 'Add some AmazingFeature')
Push to the branch (git push origin feature/AmazingFeature)
Open a Pull Request

Contribution Guidelines

Follow existing code style
Add appropriate tests
Update relevant documentation
Ensure all tests pass

📄 License

This project is licensed under the MIT License - see LICENSE file for details

🙏 Acknowledgments

Special thanks to the following projects and services:

Anthropic MCP - Model Context Protocol
arXiv API - Academic paper data
- Code repository data
Hugging Face Hub - Models and datasets
Papers with Code - Papers and code pairing

📝 Changelog

v0.1.0 (2025-10-28)

🎉 Initial Release

✅ Integrated 4 major data sources: arXiv, GitHub, Hugging Face, Papers with Code
✅ Implemented 8 MCP tools and 2 MCP resources
✅ Smart caching mechanism
✅ Coverage of 15+ AI research areas
✅ Complete documentation and examples

🗺️ Roadmap

v0.2.0 (Planned)

[ ] Add OpenReview and SemanticScholar integration
[ ] Support custom keyword subscriptions
[ ] Improve caching strategy and performance optimization
[ ] Add more unit tests

v0.3.0 (Future)

[ ] Web interface
[ ] Email notification feature
[ ] Export to PDF/HTML
[ ] Visualization charts

v1.0.0 (Long-term)

[ ] Multi-language support (full Chinese & English)
[ ] Smart recommendation algorithm
[ ] Mobile support

💬 Community

💡 Submit Feature Requests
🐛 Report Bugs
💭 Join Discussions
⭐ If you find it useful, please give us a Star!

如果这个项目对你有帮助，请给它一个 ⭐ Star！

If you find this project helpful, please give it a ⭐ Star!

Made with ❤️ by the AI Research Community

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