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PubCrawl

PubCrawl

by nickjlamb
OverviewSchemaSourceRelated ServersDiscussionsAdmin
TypeScript
Remote

resolve_drug_name

Read-only

Convert a drug name between brand and generic forms, and retrieve its drug class and common indications, using RxNorm and openFDA sources.

Instructions

Convert a brand drug name to its generic name (or a generic to its US brand names), with drug class and common indications. Deterministic — sourced from RxNorm/openFDA, no AI.

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": {
      "drug": "<drug>"
    },
    "name": "resolve_drug_name"
  }
}

Input Schema

NameRequiredDescriptionDefault
drugYesBrand or generic drug name to resolve (e.g., 'Lipitor' or 'atorvastatin')

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv2.5.1

TDQS

A4/5.0
Behavior4/5

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

The annotations already establish read-only, non-destructive behavior. The description adds genuinely useful behavioral context: it is deterministic, sourced from RxNorm/openFDA, and not AI-generated. This tells an agent to expect stable, auditable results and subtly signals that output may be limited to known drug-name mappings.

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 tight sentences with no wasted words. The action and scope are front-loaded, and the provenance/determinism note is cleanly separated. It avoids repeating schema content and earns each sentence.

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 one-parameter, read-only resolver with full schema coverage and safety annotations, the description provides the input expectations, conversion directions, and main output types. There is no output schema, and the exact return shape is not specified, but that is a minor gap for such a simple lookup 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%, so the schema fully documents the single 'drug' string parameter with examples. The description adds the directional nuance (generic → US brand names) and the output categories, but it does not need to add further input-format constraints. The baseline of 3 is appropriate.

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 opens with a clear action ('Convert'), identifies the resource (drug name), and specifies bidirectional resolution (brand→generic and generic→US brand names). It also lists additional outputs (drug class, common indications), and none of the sibling tools share exactly this responsibility, so an agent can distinguish it immediately.

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 drug-name normalization and lookup, but it never explicitly says when to prefer this tool over related siblings such as get_smpc, compare_labels, or search_by_indication. 'No AI' hints at using it when authoritative, deterministic output is wanted, but there is no explicit when/when-not guidance or mention of alternatives.

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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