Fuzzy-search the curated WGEA dataset catalog.

All seven curated datasets cover the WGEA Public Data File: per-employer workforce composition, manager movements, gender-equality policy answers, parental-leave + flexible-work policies, harm-prevention policies, employee support, and workplace overview.

Examples: # Find datasets about parental leave results = await search_datasets("parental leave") # → [{id: 'PARENTAL_LEAVE_FLEX', ...}]

# Find workforce composition by gender
results = await search_datasets("women in management")

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

Describe a dataset's filterable dimensions, returnable measures, units, and source.

Use this before calling get_data on a new dataset — it tells you the valid filter keys ('employer_name', 'anzsic_division', 'gender', ...), enumerated filter values where they exist (e.g. 'women' → 'Women'), measure aliases ('n_employees'), and the canonical source URL.

Returns: DatasetDetail with id, name, description, period_coverage, list of dimensions, list of measures, source_url, and the resolved reporting year label.

Query a curated WGEA dataset and return observations.

Examples: # Gender breakdown at Commonwealth Bank resp = await get_data( "WORKFORCE_COMPOSITION", filters={"employer_name": "Commonwealth Bank"}, )

# Promotions to manager by gender at Westpac in 2024-25
resp = await get_data(
    "WORKFORCE_MANAGEMENT",
    filters={"employer_name": "Westpac", "movement_type": "Promotions",
             "manager_category": "Managers"},
)

# Which employers in mining set gender targets?
resp = await get_data(
    "GENDER_EQUALITY_ACTIONS",
    filters={"anzsic_division": "Mining",
             "section": "Gender Pay Gap",
             "response": "Yes"},
)

# Sexual harassment policy responses across financial services
resp = await get_data(
    "HARM_PREVENTION",
    filters={"anzsic_division": "Financial and Insurance Services",
             "subsection": "Sexual Harassment"},
)

Returns: DataResponse with records (or csv), unit, reporting_year, row_count, source URL, the actual download_url used, "did you mean?" fuzzy hints if the employer-name filter didn't match exactly, and CC-BY 3.0 AU attribution.

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).

Return the N rows with the largest (or smallest) value of a measure.

Ranks across one WGEA reporting year (the latest by default, or a specific year via reporting_year=). This is the most common agent workflow — "show me the top 10 X by Y" — collapsed into a single server-side call: rank-and-slice happens on the server so the agent never has to fetch a full table just to take the top of it.

Examples: # 10 employers with the most women managers (latest reporting year) top_n("WORKFORCE_COMPOSITION", "n_employees", n=10, filters={"gender": "Women", "manager_category": "Manager"})

# 5 ANZSIC divisions with the fewest Yes responses on Gender Pay Gap
top_n("GENDER_EQUALITY_ACTIONS", "n_responses", n=5, direction="bottom",
      filters={"section": "Gender Pay Gap", "response": "Yes"})

# Top 5 employers in Mining by total workforce in 2023-24
top_n("WORKFORCE_COMPOSITION", "n_employees", n=5,
      filters={"anzsic_division": "Mining"},
      reporting_year="2023-24")

Returns: DataResponse with at most n records, sorted by measure value in the requested direction. Other fields (reporting_year, unit, attribution) match a regular get_data call.

List every curated dataset ID in this version of wgea-mcp.

Returns: Sorted list of dataset IDs.