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Retrieve data for the most recent WGEA reporting year. Filter by employer or industry to get current workforce composition, pay gap actions, or parental leave policies.

Instructions

Return rows from the most recent WGEA reporting year for a dataset.

Trims to the single latest reporting_year — useful for "what's the current gender breakdown at CBA?" without having to remember WGEA's annual cadence.

Examples: # Latest workforce composition at CBA resp = await latest("WORKFORCE_COMPOSITION", filters={"employer_name": "Commonwealth Bank"})

# Cap rows (portfolio-standard name)
resp = await latest("WORKFORCE_COMPOSITION",
                    filters={"anzsic_division": "Mining"}, limit=100)

# Legacy alias still works
resp = await latest("WORKFORCE_COMPOSITION",
                    filters={"anzsic_division": "Mining"}, max_rows=100)

Parameter notes: - Prefer limit (portfolio-standard; matches asic-mcp's latest(..., limit) parameter). - max_rows retained as legacy alias. - Supplying both raises ValueError — pick one. - get_data() keeps max_rows unchanged (separate surface, separate concern).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoCap on returned rows (portfolio-standard name). Default 2000, max 10000. Mutually exclusive with the legacy `max_rows` alias — supplying both raises ValueError.
filtersNoSame filter shape as get_data. Useful for narrowing to one employer.
max_rowsNoLegacy alias for `limit` — retained for backward compatibility (wgea-mcp <= 0.4.x). Prefer `limit` for cross-sister consistency with asic-mcp's `latest(..., limit)` parameter. Same semantics as `limit`. Supplying both raises ValueError.
dataset_idYesCurated dataset ID.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
csvNo
unitNo
queryNo
staleNo
periodNoCanonical period bounds {start, end} for cross-sister consumers. Populated alongside the wgea-specific reporting_year. For a single reporting year both bounds match; for multi-year spans they bracket the range.
sourceNoWorkplace Gender Equality Agency
recordsNo
row_countNo
dataset_idYes
source_urlYes
attributionNoSource: Workplace Gender Equality Agency. Licensed under Creative Commons Attribution 3.0 Australia (https://creativecommons.org/licenses/by/3.0/au/). Original dataset: https://data.gov.au/data/dataset/wgea-dataset
dataset_nameYes
did_you_meanNo
download_urlNo
retrieved_atYes
stale_reasonNo
truncated_atNo
reporting_yearNo
server_versionNo
Behavior4/5

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

With no annotations provided, the description carries full burden. It discloses key behavior: trimming to latest reporting_year, and mutual exclusivity of limit/max_rows. However, it does not mention potential side effects (e.g., read-only nature) or performance considerations, which are minor gaps.

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?

The description is well-organized with sections, examples, and parameter notes. Each part serves a purpose, but the length could be slightly trimmed without losing clarity. The use of code examples aids understanding.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

While the description covers most aspects, there is an inconsistency: the schema for 'limit' has default null but the description states 'Default 2000'. This could confuse an AI agent. Additionally, given the presence of an output schema, the description could reference it. Siblings are acknowledged but not fully compared.

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%, but the description adds value by clarifying preferences (limit over max_rows), labeling max_rows as legacy, and explaining mutual exclusivity. This goes beyond the schema's descriptions, providing practical usage guidance.

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 returns rows from the most recent WGEA reporting year for a dataset. It uses a specific verb ('Return rows') and resource ('from the most recent WGEA reporting year for a dataset'), and distinguishes itself from sibling tools like get_data by focusing on the latest year.

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 provides context for when to use this tool (e.g., 'what's the current gender breakdown at CBA?') and includes examples with filtering. It implicitly contrasts with get_data for all years. However, it does not explicitly state when not to use or direct to alternatives for non-latest data.

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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