MCP for Research: How to Connect AI to Research Tools

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.

# Typical workflow:
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.

# research_tracker.py
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)

# Run for each paper you want to investigate
results = gather_research_info("https://arxiv.org/abs/2103.00020")

The research tracker demonstrates systematic research discovery built from these types of scripts.