Research Tracker MCP Server

# Research Tracker MCP Server A Model Context Protocol (MCP) server that provides research inference utilities. This server extracts research metadata from paper URLs, repository links, or research names using web scraping and API integration. ## Features - Author inference from papers and repositories - Cross-platform resource discovery (papers, code, models, datasets) - Research metadata extraction (names, dates, licenses) - URL classification and relationship mapping - Comprehensive research ecosystem analysis - Rate limiting to prevent API abuse - Request caching with TTL for performance - Error handling with typed exceptions - Security validation for all URLs - Retry logic with exponential backoff ## Frontend The project includes a modern web interface built with Flask and vanilla JavaScript: - **Clean Design**: Minimalist black and white theme with soft green accents - **Real-time Discovery**: Live logging of the discovery process with scrollable output - **Responsive Layout**: Grid-based design that adapts to different screen sizes - **Interactive Elements**: Example URL buttons for quick testing - **Progress Tracking**: Visual progress indicators and status updates - **Resource Display**: Organized grid showing discovered papers, code, models, datasets, and demo spaces ### UI Components - **Input Section**: URL input field with discover button - **Discovery Log**: Real-time scrolling log of the discovery process - **Results Grid**: Clean display of discovered resources - **Example URLs**: Pre-configured test cases for demonstration - **Status Indicators**: Progress bars and status messages ## Available MCP Tools All functions are optimized for MCP usage with clear type hints and docstrings: - `infer_authors` - Extract author names from papers and repositories - `infer_paper_url` - Find associated research paper URLs - `infer_code_repository` - Discover code repository links - `infer_research_name` - Extract research project names - `classify_research_url` - Classify URL types (paper/code/model/etc.) - `infer_publication_date` - Extract publication dates - `infer_model` - Find associated HuggingFace models - `infer_dataset` - Find associated HuggingFace datasets - `infer_space` - Find associated HuggingFace spaces - `infer_license` - Extract license information - `find_research_relationships` - Comprehensive research ecosystem analysis ## Input Support - arXiv paper URLs (https://arxiv.org/abs/...) - HuggingFace paper URLs (https://huggingface.co/papers/...) - GitHub repository URLs (https://github.com/...) - HuggingFace model/dataset/space URLs - Research paper titles and project names - Project page URLs (github.io) ## MCP Best Practices Implementation This server follows official MCP best practices: 1. Security: URL validation, domain allowlisting, input sanitization 2. Performance: Request caching, rate limiting, connection pooling 3. Reliability: Retry logic, graceful error handling, timeout management 4. Documentation: Comprehensive docstrings with examples for all tools 5. Error Handling: Typed exceptions for different failure scenarios ## Environment Variables - `HF_TOKEN` - Hugging Face API token (required) - `GITHUB_AUTH` - GitHub API token (optional, enables enhanced GitHub integration) ## Usage The server automatically launches as an MCP server when run. All inference functions are exposed as MCP tools for integration with Claude and other AI assistants. ### Example Test with the 3D Arena paper: ``` Input: https://arxiv.org/abs/2506.18787 Finds: dataset (dylanebert/iso3d), space (dylanebert/LGM-tiny), and more ``` ### Rate Limits - 30 requests per minute per tool - Automatic caching reduces duplicate requests - Graceful error messages when limits exceeded ### Error Handling The server provides clear error messages: - `ValidationError`: Invalid or malicious URLs - `ExternalAPIError`: External service failures - `MCPError`: Rate limiting or other MCP issues ## Installation 1. Clone the repository 2. Install dependencies: `pip install -r requirements.txt` 3. Set environment variables 4. Run: `python app.py` ## Requirements - Python 3.8+ - See requirements.txt for dependencies ## Running the Application ### MCP Server Only ```bash python app.py ``` ### Web Interface ```bash python flask_app.py ``` The web interface will be available at `http://localhost:5000` ### Gradio Interface (Alternative) ```bash python ui.py ``` ## Project Structure - `app.py` - Main MCP server entry point - `flask_app.py` - Flask web interface - `ui.py` - Gradio alternative interface - `mcp_tools.py` - MCP tool implementations - `inference.py` - Core inference logic - `discovery.py` - Multi-round discovery functions - `static/` - CSS and JavaScript files - `templates/` - HTML templates - `utils.py` - Utility functions