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Bigred97

Australian Institute of Health and Welfare

search_datasets

Search the AIHW dataset catalog to find health and welfare datasets by free-text query, including mortality, cancer, and hospital data.

Instructions

Fuzzy-search the curated AIHW dataset catalog.

All datasets ship hand-curated in v0.1: long-term mortality (GRIM), regional mortality (MORT), cancer incidence and mortality, health expenditure, youth justice detention, and the public hospitals register.

Examples: # Find a dataset that gives deaths by cause results = await search_datasets("mortality cause of death") # → [{id: 'GRIM_DEATHS', name: 'GRIM — long-term mortality', ...}]

# Discover what's available on cancer
results = await search_datasets("cancer")

Returns: List of DatasetSummary (id, name, description, update_frequency, is_curated), ranked by relevance.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryYesFree-text search query. Matches against dataset IDs, names, descriptions, and curated search keywords. Case-insensitive.
limitNoMaximum number of results to return, ranked by relevance.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior3/5

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

No annotations provided, so description carries full burden. It discloses return format and search scope but omits performance, authentication, rate limits, or edge cases (e.g., empty query). Adequate but not comprehensive.

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?

Concise and well-structured: purpose statement, catalog overview, examples, and return format. Every sentence contributes 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?

Output schema exists, so return values are covered. Description adds search-specific context and examples. Missing edge cases and behavior for no results, but overall complete for a search tool.

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?

Schema coverage is 100% with descriptions and examples. Description adds value by specifying what fields are matched (IDs, names, descriptions, keywords) and providing example queries. Exceeds baseline 3.

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 the tool performs fuzzy-search on a curated dataset catalog, lists included datasets, and provides examples. It effectively distinguishes from siblings like list_curated or describe_dataset.

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?

Examples and the description imply use for discovery, but there is no explicit guidance on when to use this vs. siblings like describe_dataset for details or list_curated for a full list. Context is clear but lacks exclusion criteria.

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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