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fetch_abstracts

Retrieve exact PubMed records for up to 200 PMIDs, including abstracts, authors, journal, DOI, and retraction notices. Missing fields are flagged, never inferred.

Instructions

Fetch exact PubMed records (NCBI efetch) for up to 200 PMIDs.

Per record: pmid, title, authors, journal, pub_date, doi, publication_types, has_retraction_notice, abstract (structured abstracts keep their section labels), url. Any field PubMed does not provide is the literal string 'Data not provided in PubMed abstract'. PMIDs NCBI did not return are listed in not_found. Never infer or complete missing text.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
pmidsYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.2.0

TDQS

A3.6/5.0
Behavior4/5

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

With no annotations provided, the description carries the full burden and does well: it declares the missing-field sentinel string, the `not_found` behavior for PMIDs NCBI omits, the presence of a retraction-notice flag, and an explicit instruction never to infer or complete missing text. It omits auth/rate-limit behavior and any failure modes, which keeps it short of a 5.

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

Conciseness4/5

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

Front-loaded with the action, then a compact enumeration of returned fields, then two behavioral rules. The field list is long but justified because no output schema exists. No wasted sentences.

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

Completeness5/5

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

With no output schema and no annotations, the description fully specifies the return shape, the sentinel for absent data, and the `not_found` channel, plus an anti-hallucination rule. That is everything an agent needs to consume the result correctly.

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?

One parameter at 0% schema description coverage, so the description must compensate. It does add real meaning — the input is a list of PMIDs and is capped at 200 — but says nothing about PMID formatting, duplicates, or empty-list behavior. Partial compensation only.

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

Purpose4/5

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

States a specific verb and resource — fetching exact PubMed records via NCBI efetch for up to 200 PMIDs — so the agent knows exactly what it does. However, the sibling set (pubmed_fetch, pubmed_get, pubmed_batch_fetch) is highly overlapping, and the description never names or distinguishes itself from those alternatives.

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

Usage Guidelines2/5

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

No statement of when to use this tool versus pubmed_fetch, pubmed_get, or pubmed_batch_fetch, even though those siblings appear functionally similar. The only usable context is an implicit one: batch retrieval by PMID list. Nothing tells the agent when this is the wrong choice.

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