Skip to main content
Glama
Bigred97

Australian Bureau of Statistics

search_datasets

Fuzzy-search ABS dataflows using natural language. Curated indicators like unemployment and GDP rank first for common queries.

Instructions

Fuzzy-search ABS dataflow names, descriptions, and keywords.

Use this when you don't know the exact dataset ID. The 10 curated dataflows (LF, CPI, ANA_AGG, etc.) get a relevance boost so common queries like "unemployment" or "gdp" return the right dataset at rank #1 — not one of ABS's 800+ census tables that mention these keywords incidentally.

Examples: # Discover which dataflow answers "what's NSW unemployment?" results = await search_datasets("unemployment") # → [{id: 'LF', name: 'Labour Force', is_curated: True}, ...]

# Broader topic exploration
results = await search_datasets("housing", limit=5)
# → top 5 housing-related dataflows, curated first

When to use: - You have a natural-language question and need to identify the dataset - You want to discover what ABS publishes on a topic - You're not sure if a topic has a plain-English (curated) mapping yet

Returns: List of DatasetSummary (id, name, description, is_curated), ranked by relevance. Curated dataflows surface above raw SDMX dataflows.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryYesFree-text search query. Matches against dataflow IDs, names, descriptions, and each curated YAML's search_keywords. Case-insensitive.
limitNoMaximum number of results to return, ranked by relevance. Curated dataflows get a +25 score bonus so they surface above ABS's ~800 census tables for common queries.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior4/5

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

With no annotations provided, the description carries the full burden. It discloses that curated dataflows get a relevance boost (+25 score), results are ranked by relevance, and search is case-insensitive. It does not mention side effects or authentication requirements, but as a search tool, these are less critical.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Description is well-structured with sections for purpose, examples, when to use, and returns. It is reasonably concise given the detail needed, though could be slightly streamlined.

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 that an output schema exists (not shown), the description adequately handles return values by listing fields and ranking. It also explains the curated boost. For a complex search tool, it covers most relevant context.

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% and the description adds value beyond schema: explains the +25 score bonus for curated dataflows, case-insensitivity, and the matching scope (IDs, names, descriptions, keywords). Examples are provided for both parameters.

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?

Description clearly states the tool fuzzy-searches ABS dataflow names, descriptions, and keywords. It distinguishes itself from siblings (describe_dataset, get_data, latest, list_curated) by focusing on discovery when exact dataset ID is unknown.

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?

Explicitly lists when to use: when you don't know exact dataset ID, have a natural-language question, want to discover topics, or unsure if there is a curated mapping. However, it does not explicitly state when not to use or mention alternatives beyond the tool's purpose.

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

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/Bigred97/abs-mcp'

If you have feedback or need assistance with the MCP directory API, please join our Discord server