Skip to main content
Glama
Bigred97

Australian Bureau of Statistics

get_data

Retrieve Australian Bureau of Statistics observations for any dataflow. Filter by dimensions like region or measure and specify a period range for precise time-series data.

Instructions

Query an ABS dataflow and return observations.

Pass filters and/or a period range — unfiltered queries on large dataflows can return tens of thousands of observations.

Curated dataflows accept plain-English filter keys and values that are translated to SDMX codes server-side. For example, on LF: {"region": "nsw", "measure": "unemployment_rate"} resolves to SDMX key M13.3.1599.20.1.M with hidden-dim defaults auto-applied.

Examples: # NSW unemployment monthly for 2024 resp = await get_data( "LF", filters={"region": "nsw", "measure": "unemployment_rate"}, start_period="2024", end_period="2024-12", ) # → resp.records[0]: period='2024-01', value=4.8, unit='Percent'

# Multi-state comparison
resp = await get_data(
    "LF",
    filters={"region": ["nsw","vic","qld"], "measure": "unemployment_rate"},
    start_period="2024",
    format="csv",
)
# → resp.csv contains 36 rows (3 states × 12 months)

# Australia quarterly CPI annual change
resp = await get_data(
    "CPI",
    filters={"region": "australia", "measure": "change_year"},
    start_period="2020",
)

When to use: - You want observations over a time range (use latest() for the most-recent only) - You want a multi-state or multi-measure comparison via list filters - You want a CSV for downstream charting / spreadsheet tools

Returns: DataResponse with records (list of {period, value, dimensions, unit}), unit (when homogeneous), period bounds, the resolved query echo, the ABS source URL, and the CC-BY 4.0 attribution string.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
dataset_idYesABS dataflow ID like 'LF', 'CPI'. Use search_datasets to discover.
filtersNoDimension filters. For curated dataflows: plain-English keys and values, e.g. {'region': 'nsw', 'measure': 'unemployment_rate'}. For raw dataflows: SDMX dimension IDs and codes. Pass a list as the value to query multiple values for a dimension. Whitespace is stripped; empty list / empty value rejected with a hint.
start_periodNoInclusive start period. Format follows the dataflow's cadence: annual 'YYYY' (e.g. '2020'), monthly 'YYYY-MM' (e.g. '2024-03'), quarterly 'YYYY-Q1', half-yearly 'YYYY-S1', daily 'YYYY-MM-DD'. An int year (e.g. 2024) is also accepted and treated as 'YYYY'. URL-unsafe characters (?, &, /, etc.) are rejected at the boundary.
end_periodNoInclusive end period. Same format as start_period.
formatNoResponse shape. 'records' (default): flat list of observations. 'series': observations grouped by dimension key for chart-friendly shapes. 'csv': returns the table as a CSV string in the `csv` field with records empty.records

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
dataset_idYes
dataset_nameYes
queryNo
periodNo
unitNo
recordsNo
csvNo
sourceNoAustralian Bureau of Statistics
attributionNoData sourced from the Australian Bureau of Statistics and licensed under Creative Commons Attribution 4.0 International (CC BY 4.0). https://www.abs.gov.au/about/copyright-and-creative-commons
retrieved_atYes
abs_urlYes
server_versionNo
Behavior5/5

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

Despite no annotations, the description discloses key behaviors: it can return many observations, supports filter translation for curated dataflows, returns a DataResponse with records and metadata, and includes CC-BY 4.0 attribution. It also mentions error hints for empty filters.

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 well-structured with sections, examples, and code blocks. It is front-loaded with the main purpose and every sentence adds value without being verbose.

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 complexity (multiple params, nuanced filtering, period formats), the description is complete. It explains the return type DataResponse, references sibling tools for discovery, and covers all necessary usage details.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Input schema has 100% description coverage, but the description adds significant meaning: examples of plain-English vs SDMX codes, filter list behavior for multi-values, period format patterns, and format options. This goes well beyond the schema's basic descriptions.

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 queries an ABS dataflow and returns observations. It includes the verb 'query', the resource 'ABS dataflow', and distinguishes from sibling tools like 'latest' (for most-recent only) and 'describe_dataset' (for metadata).

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description includes a dedicated 'When to use' section listing three scenarios, and explicitly contrasts with 'latest()'. It also advises using 'search_datasets' to discover dataset_id and warns about unfiltered queries on large dataflows.

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