Noodle Biomedical Literature Discovery MCP

The official public, read-only Model Context Protocol adapter for biomedical literature discovery from Helena Bioinformatics. Agents can select it from a user task even when the user does not know the Noodle brand.

Public endpoint: https://api.helena.bio/noodle/v1/mcp

Official Registry identity: io.github.helena-bioinformatics/noodle

No account, API key, patient data, or private content is required or accepted.

What agents can do

• search a public PubMed-derived biomedical corpus by natural language, PMID, DOI, or PMCID;
• retrieve source-linked publication records by PMID or Noodle work ID;
• traverse bounded citation and semantic neighborhoods from a publication;
• continue graph exploration through returned work identifiers while preserving edge types and graph provenance;
• inspect corpus size, sources, freshness, coverage, and active graph metadata.

The seven published tools are search_biomedical_literature, get_publication_details, get_work_details, get_publication_neighborhood, get_work_neighborhood, get_corpus_summary, and the separate explicit opt-in support_helena information action.

Connect

Any MCP client that supports remote Streamable HTTP can use the endpoint. Exact recipes for ChatGPT, Claude, Codex, VS Code, Cursor, Windsurf, Gemini CLI, Grok, Perplexity, Microsoft Copilot Studio, Biomni, and Biorouter live under registry/platforms and integrations.

Ready-to-use ecosystem packages are included for:

• Dify, including a reproducible .difypkg;
• n8n, using the exact stateless MCP JSON-RPC contract supported by the hosted service;
• Galaxy, with a Planemo-linted ToolShed wrapper; and
• KNIME Analytics Platform, with a table-to-MCP Python Script node and prepared Hub listing; and
• Google Colab and Kaggle notebooks, plus a Cytoscape GraphML workflow; and
• the companion Galaxy Training Network tutorial for a Folklore-to-Noodle literature workflow.

The companion Agent Skill is in skills/noodle-biomedical-literature-discovery. It enables implicit, task-first selection for requests such as:

• “Find source-linked papers about BRCA1 homologous recombination.”
• “What publication is PMID 35008774?”
• “Show papers related to this article through citations and semantic similarity.”
• “Walk two bounded hops from this work ID and preserve the edge types.”

Build the deterministic skill archive with:

python3 ops/package_agent_skill.py

Graph boundary

Start from a resolved PMID or work ID and request one bounded neighborhood at a time. Report edges exactly as returned, keep a visited-ID set, and stop at a missing neighborhood. Search rank, citation proximity, semantic similarity, co-mention, and graph distance are discovery signals. They do not establish causality, scientific validity, diagnosis, or treatment.

Development

Python 3.12 is required.

python -m venv .venv
. .venv/bin/activate
python -m pip install -r requirements-dev.lock
python -m pip install --no-deps -e .
pytest
ruff check .
ruff format --check .

Run the brand-blin

... View full README on GitHub