PubMed Advanced MCP Server

batch_fetch_articles

Fetch multiple PubMed articles in large batches, respecting NCBI rate limits. Automatically chunks requests and returns both successful and failed retrievals.

Instructions

Efficiently fetch multiple articles with rate limiting.

Handles large batches by chunking requests and respecting NCBI rate limits. Returns both successful and failed retrievals.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "pmids": "<pmids>"
    },
    "name": "batch_fetch_articles"
  }
}

Input Schema

Name	Required	Description
`pmids`	Yes	List of PubMed IDs (up to 10,000)
`batch_size`	No	IDs per API call (max 500)
`include_abstract`	No	Include abstracts
`include_metadata`	No	Include article metadata

Output Schema

Name	Required	Description	Default
No arguments

Schema Changelog

Changes observed during successful MCP inspections.

First observedv1.0.0Aug 6, 2026

TDQS

A4/5.0

Behavior4/5

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

With no annotations, the description carries the burden; it reveals chunking, rate-limit handling, and the return of both successful and failed retrievals. This gives useful behavioral insight beyond the schema, though it omits details like error handling or ordering.

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 two concise sentences, front-loaded with the primary purpose, and contains no filler or redundant information.

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?

Given the presence of an output schema and full parameter documentation, the description sufficiently frames the tool's role. It could benefit from explicit alternatives or exclusions, but overall it's complete for the tool's complexity.

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?

Schema covers 100% of parameters, so baseline is 3. The description adds context that batch_size relates to chunking and rate limits, but doesn't provide any additional parameter-level detail beyond the schema.

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?

The description clearly states the tool fetches multiple articles, using a specific verb and resource. It implies batch behavior but doesn't explicitly differentiate from siblings like batch_process_articles or fetch_article_summary.

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 notes it handles large batches and respects NCBI rate limits, indicating when to use it (large-scale retrieval with rate-limit management). It doesn't name alternatives or exclusions but provides clear context for usage.

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

Deploy Server