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PubMed Advanced MCP Server

PubMed Advanced MCP Server

by SuyashEkhande
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Remote

global_search

Search all NCBI databases to find where relevant data exists. Get hit counts across 38+ databases to identify which contain matches for your query.

Instructions

Search across all NCBI databases to find data availability.

Returns hit counts for 38+ NCBI databases, helping identify which databases have relevant data for your query.

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": {
      "query": "<query>"
    },
    "name": "global_search"
  }
}

Input Schema

NameRequiredDescriptionDefault
queryYesSearch query
databasesNoSpecific databases to check (all if empty)

Output Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.0

TDQS

A4.3/5.0
Behavior4/5

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

With no annotations, the description carries the behavioral burden and does well by disclosing that it returns hit counts rather than full records, and that it covers 38+ databases. It does not mention rate limits or query syntax, but the core behavior is clearly stated.

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 exactly two sentences, front-loaded with the action, and every clause earns its place. No filler or redundancy.

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?

Given the tool's simplicity, full schema coverage, and the presence of an output schema, the description provides enough context. It explains what the tool does, what it returns (hit counts), and why it's useful.

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%, so the baseline is 3. The description adds the 'all databases' scope but does not add extra meaning beyond the schema's own parameter descriptions for 'query' and 'databases'.

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 names a specific verb ('Search') and resource ('all NCBI databases'), clearly differentiating it from siblings like pubmed_search or pmc_search that target specific databases. It also states the tool returns hit counts, which clarifies its scope.

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 provides clear context: use this tool to discover data availability across databases, which implies a discovery/overview use case. It does not explicitly name alternatives or when-not-to-use, so it misses the top bar for explicit exclusions.

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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