PubMed Advanced MCP Server

fetch_bioc_article

Retrieve PubMed or PMC articles in BioC format with pre-parsed passages and sentences, ready for NLP tasks like named entity recognition and relation extraction.

Instructions

Fetch article in BioC format for text mining.

BioC provides pre-parsed text ideal for NLP tasks:

Passage-level segmentation (title, abstract, sections)
Sentence-level boundaries
Ready for named entity recognition, relation extraction

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "fetch_bioc_article"
  }
}

Input Schema

Name	Required	Description	Default
`pmid`	No	PubMed ID (for abstract in BioC)
`pmcid`	No	PMC ID (for full-text in BioC)
`format`	No	"xml" or "json"	json

Output Schema

Name	Required	Description	Default
No arguments

Schema Changelog

Changes observed during successful MCP inspections.

First observedv1.0.0Aug 6, 2026

TDQS

A4.2/5.0

Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description must carry the behavioral disclosure burden. It does so by explaining what BioC output contains (passage segmentation, sentence boundaries) and why it is suitable for NER/relation extraction. It does not discuss error cases or side effects, but for a simple read-only fetch tool, the description is reasonably transparent.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is concise and front-loaded, with a clear imperative first sentence followed by a compact bullet list. Every line adds useful context about BioC features, with no redundant filler.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The tool has only three optional parameters, an output schema, and a clear purpose. The description complements the schema well by explaining the BioC format's value. However, it does not explicitly note that at least one of pmid or pmcid must be provided, which is a minor gap for an otherwise complete description.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema already provides 100% coverage with clear descriptions for pmid ('PubMed ID for abstract in BioC'), pmcid, and format. The description adds no additional parameter-level guidance, so the baseline of 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description opens with 'Fetch article in BioC format for text mining,' which combines a specific verb, resource, and purpose. This clearly distinguishes it from sibling tools like fetch_article_summary or fetch_full_article by emphasizing the BioC format and NLP use case.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description states 'BioC provides pre-parsed text ideal for NLP tasks' and lists segmentation/boundaries, giving clear context for when to use this tool. It does not explicitly name alternatives or state when not to use it, but the NLP framing effectively implies the appropriate use case.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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