Mcp Server Pubtator3 — independently scanned and version-tracked by SaferSkills.
SaferSkills independently audited Mcp Server Pubtator3 (Agent Skill) and scored it 100/100 (green). The audit ran 55 deterministic rules across Security, Supply Chain, Maintenance, Transparency, and Community; it found 0 high-severity and 0 lower-severity findings. The full rule-by-rule trace and per-finding evidence are below. Free, methodology-open.
Findings & checks · 0 flagged
Every scanned point with the score it earned and what moved between them.
First recorded scan — no prior version to compare against.
The primary manifest — the file an agent reads to learn what this artifact does.
This project provides an async Python server for interacting with the PubTator3 API. It exposes multiple biomedical text-mining tools compatible with the MCP agent protocol, supporting tasks such as entity lookup, biomedical literature search, and text extraction from PubMed/PMC articles.
aiohttp for non-blocking HTTP requests; designed for integration into broader MCP environments.The server provides the following tools to interact with the PubTator3 API, accessible via the MCP protocol. These tools allow programmatic access to biomedical concept lookup, literature search, full-text extraction, and entity relation discovery.
find_entityquery (string, required): Free text of the concept to look up (e.g. "breast cancer", "BRCA1").bioconcept (string, optional): Restrict results to a concept type: one of 'disease', 'gene', 'chemical', 'variant'.limit (integer, optional): Maximum number of results (default 10, max 50).A list of matching entities, each with PubTator identifiers, labels, and concept types.
search_pubtatorquery (string, required): Free text, PubTator concept ID, or a relations query.relation (string, optional): Specific relation type (default 'ANY').limit (integer, optional): Number of results to retrieve (default 10, max 50).A JSON list including article IDs and brief summaries.
get_paper_textpmid or pmcid (string, required): Article identifier (PubMed ID or PMC ID).The plain text content of the article if available.
find_related_entitiesentity_id (string, required): The PubTator entity ID to query (e.g., @GENE_BRCA1).relation_type (string, optional): Restrict relations by type (e.g., 'interacts_with', 'associated_with').entity_type (string, optional): Restrict related entities to a concept type.A pretty-printed JSON with related entity IDs and relation details.
Each tool's full input schema, description, and examples are provided in the list_tools endpoint within server.py. Use these tools to integrate sophisticated PubTator3-powered biomedical knowledge access in compatible platforms or agents.
pip install mcp-server-pubtator3 mcp-server-pubtator3~30 seconds. Free. No account. Every finding cites a rule and a line of evidence.