Academic research involves frequent research discovery: finding papers, code, related models and datasets. This typically means switching between platforms like arXiv, GitHub, and Hugging Face, manually piecing together connections.
The Model Context Protocol (MCP) is a standard that allows agentic models to communicate with external tools and data sources. For research discovery, this means AI can use research tools through natural language requests, automating platform switching and cross-referencing.
Research Discovery: Three Layers of Abstraction
Much like software development, research discovery can be framed in terms of layers of abstraction.
1. Manual Research
At the lowest level of abstraction, researchers search manually and cross-reference by hand.
1. Find paper on arXiv
2. Search GitHub for implementations
3. Check Hugging Face for models/datasets
4. Cross-reference authors and citations
5. Organize findings manually
This manual approach becomes inefficient when tracking multiple research threads or conducting systematic literature reviews. The repetitive nature of searching across platforms, extracting metadata, and cross-referencing information naturally leads to automation through scripting.
2. Scripted Tools
Python scripts automate research discovery by handling web requests, parsing responses, and organizing results.
def gather_research_info(paper_url):
paper_data = scrape_arxiv(paper_url)
github_repos = search_github(paper_data['title'])
hf_models = search_huggingface(paper_data['authors'])
return consolidate_results(paper_data, github_repos, hf_models)
results = gather_research_info("https://arxiv.org/abs/2103.00020")
The research tracker demonstrates systematic research discovery built from these types of scripts.
While scripts are faster than manual research, they often fail to automatically collect data due to changing APIs, rate limits, or parsing errors. Without human oversight, scripts may miss relevant results or return incomplete information.
3. MCP Integration
MCP makes these same Python tools accessible to AI systems through natural language.
# Example research directive
Find recent transformer architecture papers published in the last 6 months:
- Must have available implementation code
- Focus on papers with pretrained models
- Include performance benchmarks when available
The AI orchestrates multiple tools, fills information gaps, and reasons about results:
user: "Find all relevant information (code, models, etc.) on this paper: https://huggingface.co/papers/2010.11929"
ai:
This can be viewed as an additional layer of abstraction above scripting, where the “programming language” is natural language. This follows the Software 3.0 Analogy, where the natural language research direction is the software implementation.
This comes with the same caveats as scripting:
- Faster than manual research, but error-prone without human guidance
- Quality depends on the implementation
- Understanding the lower layers (both manual and scripted) leads to better implementations
Setup and Usage
Quick Setup
The easiest way to add the Research Tracker MCP is through Hugging Face MCP Settings:
- Visit huggingface.co/settings/mcp
- Search for “research-tracker-mcp” in the available tools
- Click to add it to your tools
- Follow the provided setup instructions for your specific client (Claude Desktop, Cursor, Claude Code, VS Code, etc.)
This workflow leverages the Hugging Face MCP server, which is the standard way to use Hugging Face Spaces as MCP tools. The settings page provides client-specific configuration that’s automatically generated and always up-to-date.
Learn More
Get Started:
Build Your Own:
Community:
Ready to automate your research discovery? Try the Research Tracker MCP or build your own research tools with the resources above.
