Enables searching and retrieving the latest AI/ML academic papers from arXiv, with support for filtering by keywords, research areas, and time ranges.
Provides access to GitHub's repository data for tracking trending AI projects, searching high-star repositories, and discovering open-source developments in artificial intelligence.
Allows retrieval of daily featured papers, trending models, and popular datasets from Hugging Face Hub, providing insights into the latest developments in machine learning models.
Enables access to research papers paired with their code implementations, helping discover reproducible AI research with available source code.
Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type @ followed by the MCP server name and your instructions, e.g., "@AI Research MCP Server search for recent papers on large language models from the past week"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
实时追踪 AI/LLM 研究进展的 MCP 服务器
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。
📚 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
Related MCP server: Research Tracker MCP Server
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
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
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
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）
Python 3.10+
pip 包管理器
Claude Desktop (推荐) 或其他 MCP 客户端
# 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
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 小时虽然可选，但强烈推荐配置以避免 API 速率限制
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编辑 Claude Desktop 配置文件：
操作系统 | 配置文件路径 |
macOS |
|
Windows |
|
Linux |
|
{
"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"
}
}
}
}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。
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
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: 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
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
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
API: Papers with Code API
限制: 较宽松
特点: 论文 + 代码实现
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
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
# 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# 格式化代码
black src/
# Lint 检查
ruff check src/
# 类型检查（可选）
mypy src/我们欢迎任何形式的贡献！
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
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
本项目采用 MIT 许可证 - 查看 LICENSE 文件了解详情
特别感谢以下项目和服务：
Anthropic MCP - Model Context Protocol
arXiv API - 学术论文数据
GitHub API - 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
🎉 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
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
Web 界面
邮件通知功能
导出为 PDF/HTML
可视化图表
多语言支持（完整中英文）
智能推荐算法
移动端支持
An intelligent server based on Model Context Protocol (MCP) that helps researchers and developers track the latest AI/LLM research progress in real-time.
📚 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
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
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
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
ai-research://daily-summary - Daily AI research digest (auto-cached)
ai-research://weekly-summary - Weekly AI research digest (auto-cached)
Python 3.10+
pip package manager
Claude Desktop (recommended) or other MCP clients
# 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
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 hoursAlthough optional, highly recommended to avoid API rate limits
Visit GitHub Token Settings
Click Generate new token (classic)
Select public_repo permission
Copy the generated token
Add to .env file
GITHUB_TOKEN=ghp_your_token_hereEdit Claude Desktop configuration file:
OS | Configuration File Path |
macOS |
|
Windows |
|
Linux |
|
{
"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"
}
}
}
}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.
In Claude Desktop, you can ask questions like:
Find me papers about large language models from the past weekSearch for recent research on multimodal models from the last 3 daysAny new papers on Diffusion Models?What are some new high-star LLM related repositories?Find some GitHub projects about robot learningWhat are the trending AI open source projects recently?Generate today's AI research digestShow me this week's AI research progressAny important AI news today?Find me the latest AI research in bioinformaticsSearch for latest papers and projects in reinforcement learningWhat'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
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.mdTo 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: arXiv API
Limits: Maximum 1 request per 3 seconds
Coverage: cs.AI, cs.CL, cs.LG, cs.CV, cs.RO, q-bio.*, etc.
API: GitHub REST API v3
Limits:
Without token: 60 requests/hour
With token: 5000 requests/hour
Recommendation: Configure GitHub Token
API: Hugging Face Hub API
Limits: Relatively lenient, caching recommended
Data: Daily papers, models, datasets
API: Papers with Code API
Limits: Relatively lenient
Features: Papers + code implementations
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
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
# Install dev dependencies
pip install -e ".[dev]"
# Run tests
pytest
# Run specific tests
python test_clients.py# Format code
black src/
# Lint check
ruff check src/
# Type checking (optional)
mypy src/We welcome all forms of contributions!
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
Follow existing code style
Add appropriate tests
Update relevant documentation
Ensure all tests pass
This project is licensed under the MIT License - see LICENSE file for details
Special thanks to the following projects and services:
Anthropic MCP - Model Context Protocol
arXiv API - Academic paper data
GitHub API - Code repository data
Hugging Face Hub - Models and datasets
Papers with Code - Papers and code pairing
🎉 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
Add OpenReview and SemanticScholar integration
Support custom keyword subscriptions
Improve caching strategy and performance optimization
Add more unit tests
Web interface
Email notification feature
Export to PDF/HTML
Visualization charts
Multi-language support (full Chinese & English)
Smart recommendation algorithm
Mobile support
⭐ 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
Generate a comprehensive daily summary of AI research activity
| Name | Required | Description | Default |
|---|---|---|---|
| include_papers | No | Include papers section | |
| include_repos | No | Include GitHub repos section | |
| include_models | No | Include Hugging Face models section |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It mentions 'generate' and 'comprehensive', but doesn't disclose behavioral traits such as whether this is a read-only operation, if it requires authentication, rate limits, what format the summary is in, or how it sources data. This is inadequate for a tool with no annotation coverage.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that front-loads the core purpose without unnecessary words. Every part earns its place by specifying the action and scope concisely.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no annotations, no output schema, and a tool that likely aggregates data from multiple sources, the description is incomplete. It doesn't explain what 'comprehensive' entails, how the summary is structured, or what the output looks like, leaving significant gaps for an AI agent to use it effectively.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema fully documents the three boolean parameters. The description adds no parameter-specific information beyond implying the summary includes papers, repos, and models, which aligns with the schema. Baseline 3 is appropriate as the schema does the heavy lifting.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb 'generate' and resource 'comprehensive daily summary of AI research activity', making the purpose specific and understandable. However, it doesn't explicitly differentiate from sibling tools like 'generate_weekly_summary' or 'get_daily_papers', which would require mentioning time scope or comprehensiveness distinctions.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. With siblings like 'get_daily_papers', 'get_trending_models', and 'search_by_area', there's no indication of whether this tool aggregates those or serves a different purpose, leaving usage context unclear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Generate a comprehensive weekly summary of AI research activity
| Name | Required | Description | Default |
|---|---|---|---|
| include_papers | No | Include papers section | |
| include_repos | No | Include GitHub repos section | |
| include_models | No | Include Hugging Face models section |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions 'comprehensive' but doesn't specify what that entails, such as data sources, format, length, or processing time. It fails to address potential limitations like rate limits, authentication needs, or whether the operation is read-only or has side effects.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that directly states the tool's purpose without any unnecessary words or fluff. It is appropriately sized and front-loaded, making it easy to understand at a glance.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity of generating a summary from multiple sources (papers, repos, models) and the lack of annotations and output schema, the description is insufficient. It doesn't explain what the summary includes, its format, or how it's generated, leaving significant gaps for an AI agent to understand the tool's behavior and output.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% description coverage, so the schema already documents all parameters (include_papers, include_repos, include_models) with clear descriptions. The tool description adds no additional parameter information beyond what's in the schema, which is acceptable but not additive, resulting in the baseline score.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb 'generate' and resource 'weekly summary of AI research activity', making the purpose specific and understandable. However, it doesn't explicitly distinguish this tool from its sibling 'generate_daily_summary' beyond the temporal difference, which is why it doesn't reach a perfect score.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives like 'generate_daily_summary' or other search tools. It lacks context about prerequisites, timing, or scenarios where a weekly summary is preferred over daily or other methods.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get today's featured AI papers from Hugging Face
| Name | Required | Description | Default |
|---|---|---|---|
| days | No | Number of days to look back (1-7) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool retrieves 'featured' papers, implying a curated or filtered list, but doesn't explain criteria for 'featured', potential rate limits, authentication needs, or what happens if no papers are found. This leaves significant gaps in understanding the tool's behavior.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that directly states the tool's purpose without unnecessary words. It's front-loaded and appropriately sized for a simple tool, with no wasted information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's low complexity (1 optional parameter, no output schema, no annotations), the description is minimally adequate. It covers the basic purpose but lacks details on behavioral traits, usage context, and output format, which are needed for full completeness in the absence of annotations and output schema.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% description coverage, with the 'days' parameter fully documented in the schema. The description doesn't add any parameter semantics beyond what the schema provides, such as clarifying 'today's' versus the 'days' parameter or detailing output format. Baseline 3 is appropriate as the schema handles the parameter documentation.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('Get') and resource ('today's featured AI papers from Hugging Face'), making the purpose understandable. However, it doesn't explicitly distinguish this tool from sibling tools like 'search_latest_papers' or 'search_by_area', which could also retrieve papers, so it doesn't achieve full sibling differentiation.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It doesn't mention sibling tools like 'search_latest_papers' for broader searches or 'generate_daily_summary' for summaries, nor does it specify contexts or exclusions for usage.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get trending AI models from Hugging Face
| Name | Required | Description | Default |
|---|---|---|---|
| task | No | Filter by task (e.g., 'text-generation', 'image-classification') | |
| sort | No | Sort criterion | downloads |
| limit | No | Maximum number of results |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden for behavioral disclosure. While 'Get' implies a read operation, it doesn't specify whether this requires authentication, has rate limits, returns paginated results, or provides error handling. For a tool fetching external data with no annotation coverage, this is insufficient.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that states the core purpose without any wasted words. It's appropriately sized for a simple data-fetching tool and gets straight to the point.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a tool with 3 parameters, 100% schema coverage, and no output schema, the description provides basic purpose but lacks context about behavioral traits, usage scenarios, or output format. It's minimally adequate but leaves gaps that could hinder effective tool selection and invocation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema description coverage is 100%, with all parameters well-documented in the schema itself. The description adds no additional parameter context beyond what's already in the schema, so it meets the baseline expectation without adding extra value.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('Get') and resource ('trending AI models from Hugging Face'), making the purpose immediately understandable. However, it doesn't differentiate this tool from sibling tools like 'get_trending_repos' or 'search_by_area', which appear to be related but serve different purposes.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. With siblings like 'get_trending_repos' and 'search_by_area' that might overlap in domain, there's no indication of when this specific tool is appropriate or what distinguishes it from other search/fetch tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get trending AI/ML repositories on GitHub
| Name | Required | Description | Default |
|---|---|---|---|
| period | No | Time period for trending | weekly |
| language | No | Filter by programming language | |
| max_results | No | Maximum number of results |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool retrieves trending repositories but doesn't mention any behavioral traits such as rate limits, authentication needs, data freshness, or what the output format looks like. This is a significant gap for a tool with potential external API calls.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that front-loads the core purpose without any wasted words. It's appropriately sized for a straightforward tool, making it easy for an agent to parse quickly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the lack of annotations and output schema, the description is incomplete. It doesn't explain what the tool returns (e.g., list of repos with details), any limitations, or how it interacts with siblings. For a tool that likely involves external data fetching, more context is needed to guide effective use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% description coverage, clearly documenting all three parameters with enums and defaults. The description adds no additional semantic context beyond implying filtering by AI/ML, which isn't reflected in the parameters. Baseline 3 is appropriate as the schema does the heavy lifting.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('Get') and resource ('trending AI/ML repositories on GitHub'), making the purpose immediately understandable. However, it doesn't explicitly differentiate from sibling tools like 'search_github_repos' or 'get_trending_models', which could handle similar content, so it misses the top score.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. With siblings like 'search_github_repos' and 'get_trending_models' available, there's no indication of context, exclusions, or prerequisites, leaving the agent to guess based on tool names alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Search papers and repos by research area (llm, vision, robotics, bioinfo, etc.)
| Name | Required | Description | Default |
|---|---|---|---|
| area | Yes | Research area: llm, vision, robotics, bioinfo, rl, graph, etc. | |
| days | No | Number of days to look back | |
| include_papers | No | Include papers from arXiv | |
| include_repos | No | Include GitHub repositories |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full burden but provides minimal behavioral context. It mentions searching papers and repos by area but doesn't disclose rate limits, authentication needs, result formats, pagination, or what happens with invalid areas. For a search tool with no annotation coverage, this leaves significant gaps in understanding its operation.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that front-loads the core purpose. It could be slightly more structured by explicitly separating paper and repo aspects, but it avoids redundancy and wastes no words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a search tool with 4 parameters, no annotations, and no output schema, the description is incomplete. It doesn't explain result formats, error handling, or behavioral constraints like rate limits. The combination of missing annotations and lack of output schema means the description should provide more context about what the tool returns and how it behaves.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema fully documents all parameters. The description adds no additional parameter semantics beyond implying the 'area' parameter accepts values like 'llm, vision, robotics, bioinfo', which is already covered in the schema. Baseline 3 is appropriate when schema does the heavy lifting.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool searches for papers and repositories by research area, with specific examples (llm, vision, robotics, bioinfo). It distinguishes from siblings like 'get_daily_papers' or 'search_github_repos' by combining both paper and repo search with area filtering. However, it doesn't explicitly contrast with 'search_latest_papers' or 'search_github_repos' beyond the area focus.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage when searching by research area rather than other criteria, but doesn't explicitly state when to use this tool versus alternatives like 'search_latest_papers' or 'search_github_repos'. No guidance on prerequisites, exclusions, or specific scenarios is provided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Search for trending AI/ML GitHub repositories
| Name | Required | Description | Default |
|---|---|---|---|
| keywords | No | Keywords to search for | |
| topics | No | GitHub topics to filter by (e.g., ['llm', 'transformer']) | |
| min_stars | No | Minimum number of stars | |
| days | No | Look for repos updated in last N days | |
| max_results | No | Maximum number of results |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions 'trending' but doesn't clarify behavioral traits like how results are sorted, rate limits, authentication needs, or what data is returned. This leaves significant gaps in understanding the tool's operation and constraints.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that directly states the tool's purpose. It's appropriately sized and front-loaded, with no wasted words, though it could be slightly more informative without losing conciseness.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (5 parameters, no annotations, no output schema), the description is incomplete. It lacks details on behavioral traits, output format, and usage context, making it inadequate for an agent to fully understand how to invoke and interpret results from this search tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% description coverage, clearly documenting all 5 parameters. The description adds no additional meaning beyond the schema, such as explaining parameter interactions or search logic. Baseline score of 3 is appropriate as the schema adequately covers parameter semantics without extra value from the description.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description states the tool searches for trending AI/ML GitHub repositories, which provides a general purpose. However, it's vague about what 'trending' means (e.g., based on stars, recency, or other metrics) and doesn't clearly distinguish it from sibling tools like 'get_trending_repos' or 'search_by_area', leaving ambiguity in scope and differentiation.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No explicit guidance is provided on when to use this tool versus alternatives. The description doesn't mention sibling tools, prerequisites, or specific contexts for application, such as comparing to 'get_trending_repos' for broader trending or 'search_by_area' for non-AI/ML searches, leaving the agent without clear usage direction.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Search for latest AI/ML research papers from multiple sources (arXiv, Papers with Code, Hugging Face)
| Name | Required | Description | Default |
|---|---|---|---|
| keywords | No | Keywords to search for (e.g., ['LLM', 'multimodal']) | |
| days | No | Number of days to look back (1-30) | |
| sources | No | Data sources to search (default: all) | |
| max_results | No | Maximum number of results per source |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions the sources but doesn't describe key behaviors such as rate limits, authentication needs, pagination, error handling, or the format of returned results. For a search tool with multiple parameters and no output schema, this leaves significant gaps in understanding how the tool operates.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that directly states the tool's purpose and scope without any redundant information. It's front-loaded with the core functionality and specifies the sources concisely, making it easy for an agent to parse quickly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (4 parameters, no annotations, no output schema), the description is incomplete. It lacks information on behavioral traits, output format, and usage context relative to siblings. While concise, it doesn't provide enough detail for an agent to fully understand how to invoke and interpret results from this tool effectively.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the input schema fully documents all four parameters (keywords, days, sources, max_results) with descriptions and defaults. The description adds no additional parameter semantics beyond what's in the schema, meeting the baseline for high schema coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('search for latest AI/ML research papers') and resources ('from multiple sources'), specifying the domains (arXiv, Papers with Code, Hugging Face). However, it doesn't explicitly differentiate from sibling tools like 'search_by_area' or 'get_daily_papers', which might have overlapping functionality.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives like 'search_by_area' or 'get_daily_papers'. It mentions the sources but doesn't explain why one would choose this tool over others, leaving the agent to infer usage context from the tool name alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
generate_daily_summarygenerate_weekly_summaryget_daily_papersget_trending_modelsget_trending_repossearch_by_areasearch_github_repossearch_latest_papersScored across 8 tools
Some tools have clear distinctions (e.g., generate_daily_summary vs. get_daily_papers), but there is notable overlap between get_trending_repos and search_github_repos, and between get_daily_papers and search_latest_papers, which could cause confusion. The descriptions help differentiate, but the boundaries are not entirely clear.
Most tools follow a consistent verb_noun pattern (e.g., generate_daily_summary, get_trending_models), with minor deviations like search_by_area (which uses 'by' instead of a direct noun). Overall, the naming is readable and predictable, though not perfectly uniform.
With 8 tools, the count is reasonable for an AI research server, covering summary generation, data retrieval, and search functions. It is slightly on the higher side but well within a manageable scope, with each tool serving a distinct purpose in the domain.
The toolset covers key areas like summaries, trending items, and searches, but there are gaps in CRUD operations (e.g., no tools for saving, updating, or deleting research data) and limited coverage of non-Hugging Face/GitHub sources. It supports core workflows but may leave agents needing additional functionality for comprehensive research management.
Research intelligence for AI coding agents. 2M+ CS papers with evidence and tradeoffs.
Search 8.5M scientific papers with LLM TLDRs, citations, linked entities, figures, and full text.
AI research intelligence for AI, ML, NLP, vision and robotics with citations and entity graphs.
Extract papers from ArXiv — titles, abstracts, authors, categories & PDF links. Monitor new AI, phys
