PubMed Advanced MCP Server

batch_process_articles

Process large PubMed article collections via batch operations, handling summaries, full records, BioC export, and text statistics with chunked processing and rate limiting.

Instructions

Process large sets of articles with batch operations.

Handles datasets of 10K+ articles efficiently with chunked processing and rate limiting.

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": {
      "input_source": "<input_source>"
    },
    "name": "batch_process_articles"
  }
}

Input Schema

Name	Required	Description	Default
`operation`	No	- "fetch_summaries": Get article metadata - "fetch_full": Get full records - "export_bioc": Export in BioC format - "text_statistics": Compute text statistics	fetch_summaries
`batch_config`	No	{"batch_size": 100, "parallel_workers": 3}
`input_source`	Yes	Data source specification - {"from_search": {"query": "...", "database": "pubmed"}} - {"from_ids": ["pmid1", "pmid2", ...]} - {"from_pipeline": {"query_key": "1", "web_env": "..."}}
`output_format`	No	"json", "csv", or "ndjson"	json

Output Schema

Name	Required	Description	Default
No arguments

Schema Changelog

Changes observed during successful MCP inspections.

First observedv1.0.0Aug 6, 2026

TDQS

B3.3/5.0

Behavior3/5

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

With no annotations, the description carries the burden. It discloses chunked processing and rate limiting, which are useful behavioral traits, but it does not mention whether operations are read-only, require authentication, or how errors are handled. This is partial transparency.

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 short sentences that are front-loaded with the purpose and add one key detail about handling scale and rate limiting. There is no wasted text.

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 rich input schema (all parameters described, nested objects explained) and the presence of an output schema, the description covers the high-level usage and scale. It could mention the specific operation types, but those are already in the schema, so the description is adequate for a complex tool.

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 description coverage is 100%, and the schema already documents all four parameters with examples and allowed values. The description adds no additional meaning about parameters, so the baseline of 3 applies.

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

Purpose3/5

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

The description states it processes large sets of articles with batch operations, but 'process' is a generic verb that does not specify the actual operations (fetch summaries, export BioC, etc.). It identifies the resource (articles) and scale, but lacks a specific action, making it vague and not clearly distinguishing from siblings like batch_fetch_articles.

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

Usage Guidelines3/5

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

The description implies usage for large datasets ('10K+ articles') and mentions efficiency, which provides some context. However, it does not explicitly state when to use this tool over alternatives like batch_fetch_articles, nor does it give exclusion criteria.

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

Deploy Server