AI Research MCP Server

<div align="center"> # 🔬 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) [](https://modelcontextprotocol.io) [English](#english) | [中文](#chinese) </div> --- <a name="chinese"></a> ## 📖 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 1. **search_latest_papers**: 搜索最新 AI 论文 2. **search_github_repos**: 搜索热门 AI GitHub 仓库 3. **get_daily_papers**: 获取今日精选论文 4. **get_trending_repos**: 获取 GitHub trending 仓库 5. **get_trending_models**: 获取 Hugging Face 热门模型 6. **search_by_area**: 按研究领域搜索（LLM、视觉、机器人等） 7. **generate_daily_summary**: 生成每日 AI 研究汇总 8. **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 ```bash # 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](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： ```bash # 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 <details> <summary>点击展开配置步骤</summary> 1. 访问 [GitHub Token Settings](https://github.com/settings/tokens) 2. 点击 `Generate new token (classic)` 3. 勾选 `public_repo` 权限 4. 复制生成的 token 5. 添加到 `.env` 文件 ```bash GITHUB_TOKEN=ghp_your_token_here ``` </details> ## 💬 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` | <details> <summary>方式 1: 使用 Python 命令（推荐）</summary> ```json { "mcpServers": { "ai-research": { "command": "python", "args": ["-m", "ai_research_mcp.server"], "env": { "GITHUB_TOKEN": "your_github_token_here" } } } } ``` </details> <details> <summary>方式 2: 使用绝对路径</summary> ```json { "mcpServers": { "ai-research": { "command": "C:\\Users\\YourName\\path\\to\\python.exe", "args": ["-m", "ai_research_mcp.server"], "env": { "GITHUB_TOKEN": "your_github_token_here" } } } } ``` </details> ### 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： <details open> <summary><b>🔍 搜索最新论文</b></summary> ``` 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？ ``` </details> <details> <summary><b>💻 查找 GitHub 仓库</b></summary> ``` 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？ ``` </details> <details> <summary><b>📊 获取每日汇总</b></summary> ``` 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？ ``` </details> <details> <summary><b>🎯 按领域搜索</b></summary> ``` 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？ ``` </details> <details> <summary><b>🤖 追踪模型</b></summary> ``` 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？ ``` </details> > 💡 查看 [EXAMPLES.md](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 <details> <summary><b>❓ 为什么搜索结果为空？</b></summary> 可能原因： 1. 关键词太具体 → 尝试使用更通用的术语 2. 时间范围太短 → 增加 `days` 参数 3. API 速率限制 → 等待几分钟后重试 4. 网络问题 → 检查网络连接 </details> <details> <summary><b>⚠️ GitHub API 速率限制错误</b></summary> **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 </details> <details> <summary><b>🚫 服务器启动失败</b></summary> 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 </details> <details> <summary><b>🔄 缓存数据过时</b></summary> 删除缓存目录重新获取： ```bash # Linux/macOS rm -rf .cache # Windows rmdir /s .cache ``` </details> > 🆘 更多问题？查看 [TROUBLESHOOTING.md](TROUBLESHOOTING.md) 或 [提交 Issue](https://github.com/nanyang12138/AI-Research-MCP/issues) ## 👨‍💻 开发 ### 运行测试 ```bash # 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 ```bash # 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 1. Fork 本仓库 2. 创建你的特性分支 (`git checkout -b feature/AmazingFeature`) 3. 提交你的更改 (`git commit -m 'Add some AmazingFeature'`) 4. 推送到分支 (`git push origin feature/AmazingFeature`) 5. 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](LICENSE) 文件了解详情 ## 🙏 致谢 特别感谢以下项目和服务： - [Anthropic MCP](https://modelcontextprotocol.io) - Model Context Protocol - [arXiv API](https://arxiv.org/help/api) - 学术论文数据 - [GitHub API](https://docs.github.com/en/rest) - 代码仓库数据 - [Hugging Face Hub](https://huggingface.co) - 模型和数据集 - [Papers with Code](https://paperswithcode.com) - 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](https://github.com/nanyang12138/AI-Research-MCP/issues/new?labels=enhancement) - 🐛 [报告 Bug](https://github.com/nanyang12138/AI-Research-MCP/issues/new?labels=bug) - 💭 [参与讨论](https://github.com/nanyang12138/AI-Research-MCP/discussions) - ⭐ 如果觉得有用，请给我们一个 Star！ --- <a name="english"></a> ## 🌐 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 1. **search_latest_papers** - Search latest AI papers 2. **search_github_repos** - Search trending AI GitHub repositories 3. **get_daily_papers** - Get today's featured papers 4. **get_trending_repos** - Get GitHub trending repositories 5. **get_trending_models** - Get Hugging Face trending models 6. **search_by_area** - Search by research area (LLM, Vision, Robotics, etc.) 7. **generate_daily_summary** - Generate daily AI research digest 8. **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 ```bash # 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](QUICKSTART.md) for detailed installation guide ## ⚙️ Configuration ### Environment Variables (Optional) Create a `.env` file: ```bash # 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 <details> <summary>Click to expand setup steps</summary> 1. Visit [GitHub Token Settings](https://github.com/settings/tokens) 2. Click `Generate new token (classic)` 3. Select `public_repo` permission 4. Copy the generated token 5. Add to `.env` file ```bash GITHUB_TOKEN=ghp_your_token_here ``` </details> ## 💬 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` | <details> <summary>Method 1: Using Python Command (Recommended)</summary> ```json { "mcpServers": { "ai-research": { "command": "python", "args": ["-m", "ai_research_mcp.server"], "env": { "GITHUB_TOKEN": "your_github_token_here" } } } } ``` </details> <details> <summary>Method 2: Using Absolute Path</summary> ```json { "mcpServers": { "ai-research": { "command": "C:\\Users\\YourName\\path\\to\\python.exe", "args": ["-m", "ai_research_mcp.server"], "env": { "GITHUB_TOKEN": "your_github_token_here" } } } } ``` </details> ### 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: <details open> <summary><b>🔍 Search Latest Papers</b></summary> ``` 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? ``` </details> <details> <summary><b>💻 Find GitHub Repositories</b></summary> ``` 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? ``` </details> <details> <summary><b>📊 Get Daily Digest</b></summary> ``` Generate today's AI research digest ``` ``` Show me this week's AI research progress ``` ``` Any important AI news today? ``` </details> <details> <summary><b>🎯 Search by Domain</b></summary> ``` Find me the latest AI research in bioinformatics ``` ``` Search for latest papers and projects in reinforcement learning ``` ``` What's new in computer vision? ``` </details> <details> <summary><b>🤖 Track Models</b></summary> ``` What are the trending new models on Hugging Face? ``` ``` Any popular text generation models recently? ``` ``` Any newly released open-source LLMs? ``` </details> > 💡 See [EXAMPLES.md](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 <details> <summary><b>❓ Why are search results empty?</b></summary> Possible reasons: 1. Keywords too specific → Try more general terms 2. Time range too short → Increase `days` parameter 3. API rate limit → Wait a few minutes and retry 4. Network issues → Check network connection </details> <details> <summary><b>⚠️ GitHub API Rate Limit Error</b></summary> **Solution**: Configure `GITHUB_TOKEN` environment variable Rate limit comparison: - ❌ Without Token: 60 requests/hour - ✅ With Token: 5000 requests/hour </details> <details> <summary><b>🚫 Server Startup Failed</b></summary> Checklist: - [ ] Python version >= 3.10 - [ ] Dependencies installed: `pip install -e .` - [ ] Configuration file path correct - [ ] Environment variables set correctly </details> <details> <summary><b>🔄 Cached Data Outdated</b></summary> Delete cache directory to refresh: ```bash # Linux/macOS rm -rf .cache # Windows rmdir /s .cache ``` </details> > 🆘 More issues? Check [TROUBLESHOOTING.md](TROUBLESHOOTING.md) or [Submit an Issue](https://github.com/nanyang12138/AI-Research-MCP/issues) ## 👨‍💻 Development ### Running Tests ```bash # Install dev dependencies pip install -e ".[dev]" # Run tests pytest # Run specific tests python test_clients.py ``` ### Code Formatting ```bash # Format code black src/ # Lint check ruff check src/ # Type checking (optional) mypy src/ ``` ## 🤝 Contributing We welcome all forms of contributions! ### How to Contribute 1. Fork this repository 2. Create your feature branch (`git checkout -b feature/AmazingFeature`) 3. Commit your changes (`git commit -m 'Add some AmazingFeature'`) 4. Push to the branch (`git push origin feature/AmazingFeature`) 5. 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](LICENSE) file for details ## 🙏 Acknowledgments Special thanks to the following projects and services: - [Anthropic MCP](https://modelcontextprotocol.io) - Model Context Protocol - [arXiv API](https://arxiv.org/help/api) - Academic paper data - [GitHub API](https://docs.github.com/en/rest) - Code repository data - [Hugging Face Hub](https://huggingface.co) - Models and datasets - [Papers with Code](https://paperswithcode.com) - 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](https://github.com/nanyang12138/AI-Research-MCP/issues/new?labels=enhancement) - 🐛 [Report Bugs](https://github.com/nanyang12138/AI-Research-MCP/issues/new?labels=bug) - 💭 [Join Discussions](https://github.com/nanyang12138/AI-Research-MCP/discussions) - ⭐ If you find it useful, please give us a Star! --- <div align="center"> **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 </div>