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 typical that enables agentic models to speak with external tools and data sources. For research discovery, this implies AI can use research tools through natural language requests, automating platform switching and cross-referencing.
Research Discovery: Three Layers of Abstraction
Very like software development, research discovery will be framed by way of layers of abstraction.
1. Manual Research
At the bottom 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 results in 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 a majority of these scripts.
While scripts are faster than manual research, they often fail to mechanically collect data as a result of 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 within the last 6 months:
- Will need to have available implementation code
- Concentrate 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 will be viewed as a further 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 identical caveats as scripting:
- Faster than manual research, but error-prone without human guidance
- Quality is dependent upon the implementation
- Understanding the lower layers (each manual and scripted) leads to raised implementations
Setup and Usage
Quick Setup
The best solution to add the Research Tracker MCP is thru Hugging Face MCP Settings:
- Visit huggingface.co/settings/mcp
- Seek for “research-tracker-mcp” within the available tools
- Click so as to add it to your tools
- Follow the provided setup instructions on your specific client (Claude Desktop, Cursor, Claude Code, VS Code, etc.)
This workflow leverages the Hugging Face MCP server, which is the usual solution to use Hugging Face Spaces as MCP tools. The settings page provides client-specific configuration that is mechanically generated and all the time up-to-date.
Learn More
Get Began:
Construct Your Own:
Community:
Able to automate your research discovery? Try the Research Tracker MCP or construct your individual research tools with the resources above.
