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berntpopp
by berntpopp

Get Diseases (batch)

get_disease_batch
Read-onlyIdempotent

Retrieve multiple rare disease records in a single API call using ORPHAcodes, labels, or cross-reference identifiers. Supports sparse field projection to return only needed data.

Instructions

Fetch many disease records in one call (partial success per item: each row is the record or its own ok=false/error_code/message). Each term accepts an ORPHAcode, label, or xref CURIE; pass fields=[...] for a sparse projection. Max 50 items; compact per item. Signature: get_disease_batch(terms, response_mode=, fields=).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
termsYes1..50 ids/labels/xrefs.
fieldsNoSparse fieldset: return ONLY these top-level keys (dot into a grouped object, e.g. 'xrefs.OMIM'). Identity anchors (orpha_code, name, orphanet_version) are always included. Omit for the full payload.
response_modeNoVerbosity: minimal|compact|standard|full (default compact).compact

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
hintNo
_metaNo
countNo
fieldNo
messageNo
resultsNo
successNo
retryableNo
candidatesNo
error_codeNo
allowed_valuesNo
recovery_actionNo
Behavior4/5

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

Annotations already declare idempotency and read-only. The description adds the crucial partial success pattern, input flexibility, and sparse projection capability, enriching understanding beyond annotations.

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 substantive sentences plus a signature, front-loading key behavior and constraints. Every sentence adds value with no redundancy.

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 complex batch operation and presence of an output schema, the description adequately covers core behavior (partial success, input constraints). It could mention pagination or rate limits, but the current level suffices for selection and invocation.

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?

The input schema has full coverage (100%). The description adds practical context: acceptable term types and projection usage, plus the function signature, providing extra meaning over raw schema.

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 clearly states it fetches many disease records in one call, with distinct behavior (partial success per item). This distinguishes it from siblings like get_disease (single) and resolve_disease_batch (resolution).

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 specifies when to use (batch fetch), acceptable input types (ORPHAcode, label, xref), and constraints (max 50 items). It lacks explicit exclusions or alternatives but provides sufficient context.

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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