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AI Research MCP Server

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🔬 AI Research MCP Server

实时追踪 AI/LLM 研究进展的 MCP 服务器

Python Version License MCP

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

  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

# 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

  1. 访问 GitHub Token Settings

  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

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

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

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？
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？
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？
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？
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

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

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

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

删除缓存目录重新获取：

# Linux/macOS rm -rf .cache # Windows rmdir /s .cache

🆘 更多问题？查看 TROUBLESHOOTING.md 或 提交 Issue

👨‍💻 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

  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 文件了解详情

🙏 致谢

特别感谢以下项目和服务：

📝 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

🌐 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

# 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

  1. Visit GitHub Token Settings

  2. Click Generate new token (classic)

  3. Select public_repo permission

  4. Copy the generated token

  5. 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

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

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?
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?
Generate today's AI research digest
Show me this week's AI research progress
Any important AI news today?
Find me the latest AI research in bioinformatics
Search for latest papers and projects in reinforcement learning
What's new in computer vision?
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

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

Solution: Configure GITHUB_TOKEN environment variable

Rate limit comparison:

  • ❌ Without Token: 60 requests/hour

  • ✅ With Token: 5000 requests/hour

Checklist:

  • Python version >= 3.10

  • Dependencies installed: pip install -e .

  • Configuration file path correct

  • Environment variables set correctly

Delete cache directory to refresh:

# Linux/macOS rm -rf .cache # Windows rmdir /s .cache

🆘 More issues? Check TROUBLESHOOTING.md or Submit an Issue

👨‍💻 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

  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 file for details

🙏 Acknowledgments

Special thanks to the following projects and services:

📝 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

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

Deploy Server
A
security – no known vulnerabilities
A
license - permissive license
A
quality - confirmed to work

How are these scores calculated?

remote-capable server

The server can be hosted and run remotely because it primarily relies on remote services or has no dependency on the local environment.

Tools

Enables real-time tracking of AI/LLM research progress by searching and aggregating content from arXiv, GitHub, Hugging Face, and Papers with Code. Supports intelligent search, automated daily/weekly research summaries, and covers 15+ AI research areas with smart caching.

  1. 📖 简介
    1. 🎯 核心功能
  2. ✨ 功能特点
    1. 📚 多数据源集成
    2. 🎯 覆盖的 AI 研究领域
    3. 🛠️ MCP 工具
    4. 📊 MCP 资源
  3. 🚀 快速开始
    1. 前置要求
    2. 安装步骤
  4. ⚙️ 配置
    1. 环境变量（可选）
    2. 🔑 获取 GitHub Token
  5. 💬 在 Claude Desktop 中使用
    1. 配置 Claude Desktop
    2. 重启 Claude Desktop
  6. 📖 使用示例
    1. 技术架构
      1. 项目结构
      2. 缓存机制
    2. API 数据源
      1. arXiv
      2. GitHub
      3. Hugging Face
      4. Papers with Code
    3. 🔧 故障排除
      1. 👨‍💻 开发
        1. 运行测试
        2. 代码格式化
      2. 🤝 贡献
        1. 如何贡献
        2. 贡献指南
      3. 📄 许可证
        1. 🙏 致谢
          1. 📝 更新日志
            1. v0.1.0 (2025-10-28)
          2. 🗺️ 路线图
            1. v0.2.0 (计划中)
            2. v0.3.0 (未来)
            3. v1.0.0 (长期)
          3. 💬 社区
            1. 🌐 English Version
              1. 📖 Introduction
                1. 🎯 Core Features
              2. ✨ Features
                1. 📚 Multi-source Data Integration
                2. 🎯 Covered AI Research Areas
                3. 🛠️ MCP Tools
                4. 📊 MCP Resources
              3. 🚀 Quick Start
                1. Prerequisites
                2. Installation Steps
              4. ⚙️ Configuration
                1. Environment Variables (Optional)
                2. 🔑 Getting GitHub Token
              5. 💬 Using with Claude Desktop
                1. Configure Claude Desktop
                2. Restart Claude Desktop
              6. 📖 Usage Examples
                1. 🏗️ Technical Architecture
                  1. Project Structure
                  2. Caching Mechanism
                2. 🌐 API Data Sources
                  1. arXiv
                  2. GitHub
                  3. Hugging Face
                  4. Papers with Code
                3. 🔧 Troubleshooting
                  1. 👨‍💻 Development
                    1. Running Tests
                    2. Code Formatting
                  2. 🤝 Contributing
                    1. How to Contribute
                    2. Contribution Guidelines
                  3. 📄 License
                    1. 🙏 Acknowledgments
                      1. 📝 Changelog
                        1. v0.1.0 (2025-10-28)
                      2. 🗺️ Roadmap
                        1. v0.2.0 (Planned)
                        2. v0.3.0 (Future)
                        3. v1.0.0 (Long-term)
                      3. 💬 Community

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