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u9401066

pubmed-search-mcp

by u9401066
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Remote

find_related_articles

Read-onlyIdempotent

Locate articles similar to a given PubMed paper using PubMed's Related Articles feature to discover research by topic, MeSH terms, or authors.

Instructions

Find articles related to a given PubMed article. Uses PubMed's "Related Articles" feature to find similar papers.

═══════════════════════════════════════════════════════════════ 🔗 CITATION NETWORK EXPLORATION WORKFLOW ═══════════════════════════════════════════════════════════════

This is ONE of THREE tools for exploring citation networks:

1️⃣ find_related_articles() ← YOU ARE HERE │ 📌 Algorithm-based similarity (like PubMed "Similar Articles") │ 📌 Finds papers with similar topics, MeSH terms, authors │ 📌 Good for: Discovering related research you might have missed └─► Returns: Similar papers (not based on citations)

2️⃣ find_citing_articles() │ 📌 Forward citation search (who cited THIS paper?) │ 📌 Finds papers published AFTER the source article │ 📌 Good for: Tracking impact, finding follow-up studies └─► Returns: Papers that cite this article

3️⃣ get_article_references() │ 📌 Backward citation search (what did THIS paper cite?) │ 📌 Finds papers published BEFORE the source article │ 📌 Good for: Finding foundational papers, methodology sources └─► Returns: This article's bibliography

═══════════════════════════════════════════════════════════════ EXAMPLE WORKFLOW: ═══════════════════════════════════════════════════════════════

Step 1: Start with a key paper find_related_articles(pmid="23132851") → Find similar research directions

Step 2: Explore backward (foundations) get_article_references(pmid="23132851") → Find the foundational papers it builds on

Step 3: Explore forward (impact) find_citing_articles(pmid="23132851") → Find how the field developed after this paper

Args: pmid: PubMed ID of the source article ("12345678" or "PMID:12345678"). limit: Maximum number of related articles to return (1-50, default: 5).

Returns: List of related articles with details.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
pmidYes
limitNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed8 schema fields changedv0.7.2
    • addedInput schema / additionalProperties
      Added value: +false
    • addedInput schema / properties / limit / maximum
      Added value: +50
    • addedInput schema / properties / limit / minimum
      Added value: +1
    • removedInput schema / properties / pmid / anyOf
      Removed value: -[
-  {
-    "type": "string"
-  },
-  {
-    "type": "integer"
-  }
-]
    • addedInput schema / properties / pmid / maxLength
      Added value: +512
    • addedInput schema / properties / pmid / minLength
      Added value: +1
    • addedInput schema / properties / pmid / type
      Added value: +"string"
    • changedOutput schema / (root)
      Previous value: -{
-  "properties": {
-    "result": {
-      "title": "Result",
-      "type": "string"
-    }
-  },
-  "required": [
-    "result"
-  ],
-  "title": "find_related_articlesOutput",
-  "type": "object"
-}New value: +null
  2. First observedv0.5.16

TDQS

A4.2/5.0
Behavior3/5

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

Annotations already declare readOnly=true, openWorld=true, idempotent=true and destructive=false, so the safety/profile burden is largely lifted. The description adds genuinely useful behavioral context: the results are algorithm-based similarity (not citation-based), which matters for interpreting output. It doesn't cover error behavior (e.g. invalid PMID) or pagination, but with annotations carrying the safety profile, a 3 is fair.

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

Conciseness3/5

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

The description is heavily front-loaded and clearly structured, but it is quite verbose for a 2-parameter tool, and a large portion of the diagram (the full workflow) belongs to sibling context rather than this tool's own behavior. Still, the formatting aids scanning. Adequate but oversized.

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?

For a read-only, 2-param lookup tool with no output schema, the description is nearly complete: it explains the mechanism, the return concept ('similar papers, not citation-based'), and routing to siblings. The only real gap is that it doesn't explain the shape of returned article details, which matters slightly given no output schema exists.

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

Parameters4/5

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

Schema description coverage is 0%, so the description must carry parameter meaning. It documents pmid with accepted formats ('12345678' or 'PMID:12345678') and the limit range (1-50, default 5), which the bare schema does not. This meaningfully compensates for the zero-coverage schema, though it could clarify whether limit=50 hits a cap or truncates silently.

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 states a specific verb and resource ('Find articles related to a given PubMed article') and explicitly differentiates itself from the two sibling citation tools (find_citing_articles, get_article_references) via the workflow diagram, naming the underlying mechanism (PubMed's 'Related Articles' algorithm). An agent can immediately tell what this does and how it differs from siblings.

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

Usage Guidelines5/5

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

The numbered workflow diagram explicitly maps each of the three citation tools to its use case: this one for discovering similar research/related directions, find_citing_articles for impact/forward citations, get_article_references for foundations. It even includes a step-by-step example workflow showing when to reach for each. This is about as explicit as when/when-not guidance gets.

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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