A
licenseA
qualityB
maintenanceMCP server for Australian Institute of Health and Welfare statistics — plain-English access to mortality, cancer, hospital, and health expenditure data via data.gov.au.
6
MIT
wgea-mcp
by Bigred97
This server provides plain-English MCP access to WGEA's Australian workplace gender equality data, letting you search, describe, query, and rank curated datasets on per-employer workforce composition, gender pay gaps, policies, and more.
Search datasets – fuzzy-search the curated catalog by keywords like "parental leave" or "pay gap".
Describe datasets – list filterable dimensions, measures, units, and source URLs before querying.
Query data – filter by employer, ANZSIC division, gender, manager category, policy responses, etc., with period ranges and records/series/csv output.
Get latest year – restrict queries to the most recent WGEA reporting year, with row caps via
limit(or legacymax_rows).Top N rankings – return the largest or smallest rows by a numeric measure (e.g.
n_employees,n_responses) for the latest or a specified year.List curated datasets – enumerate all available dataset IDs.
Fuzzy employer lookup – resolve abbreviations like "CBA", "NAB", "Woolies" to legal names, with
did_you_meansuggestions when no exact match exists.Attribution and reliability – every response includes source attribution, download URLs, staleness flags, and server version; live CKAN URL resolution with a bundled fallback seed manifest.
wgea-mcp
mcp-name: io.ausdata/wgea-mcp
MCP server for the Workplace Gender Equality Agency (WGEA) public data file. Plain-English access to per-employer workforce composition, gender-equality policy answers, parental leave, flexible work, and harm-prevention data — every WGEA-reporting employer in Australia (~9,600 employers), every year, from a single uvx command.
Hosted access? For cross-source queries, webhooks, an always-on REST API, and a uniform response envelope across all 9 sources, see ausdata.io — free tier available (500 calls/mo, no card).
"What's the gender breakdown at Commonwealth Bank?"
"Which mining companies set gender targets in 2024-25?"
"Workforce composition by occupation at Qantas"
"Sexual harassment policy responses across financial services"
"Promotions to manager by gender at Atlassian"Sister to abs-mcp, rba-mcp, ato-mcp, apra-mcp, aihw-mcp, asic-mcp, and au-weather-mcp.
Install
uvx --upgrade wgea-mcpClaude Desktop
{
"mcpServers": {
"wgea": { "command": "uvx", "args": ["--upgrade", "wgea-mcp"] }
}
}Claude Code
claude mcp add wgea --command uvx --args -- --upgrade wgea-mcpRelated MCP server: aemo-mcp
What it exposes
Six tools, all plain-English in, structured out:
Tool | Purpose |
| Fuzzy-search the curated catalog by keyword |
| List a dataset's filterable dimensions and returnable measures |
| Query with |
| Restrict to the latest reporting year. Row cap parameter is |
| Rank rows by a numeric measure (e.g. |
| Enumerate the curated dataset IDs |
Every response is the same shape — dataset_id, dataset_name, query, reporting_year, unit, row_count, records, source_url, download_url, did_you_mean, attribution, stale flag, server_version.
Curated datasets (8)
ID | What it is | Source |
| Industry gender pay gap mid-points by ANZSIC division + national rollup | EGPG xlsx on |
| Per-employer headcount by occupation × manager category × gender |
|
| Manager movements (promotions, hires, resignations) by gender |
|
| Pay-gap analyses, gender targets, governance — Q&A responses |
|
| Parental leave + flexible-work policy responses |
|
| Sexual harassment + domestic-violence policy responses |
|
| Carer leave, EAP, mental-health programs |
|
| Board composition, governing-body diversity, CEO + KMP demographics |
|
The headline gender pay-gap %.
HEADLINE_GAP(added in 0.6.0) carries the industry mid-points WGEA publishes annually in its Employer Gender Pay Gaps Report — e.g. Mining 18.9%, Financial and Insurance Services 21.4%, "All employers" national mid-point 11.2% (2024-25 reporting year). Numbers match WGEA's published Figure 4 to within rounding. For the workforce-weighted national aggregate (~21.1% private sector) WGEA states in its public materials, see the WGEA Data Explorer — that figure requires payroll data WGEA holds before public release and is not derivable from the published employer-level xlsx.
Reliability — 2-tier URL resolution
WGEA publishes the public data file annually under a single CKAN package on data.gov.au. Each annual release gets a fresh resource UUID:
Live CKAN —
package_show?id=wgea-datasetreturns every resource; the newest "WGEA Data — Public Data File" wins. Cached 6h.Bundled seed manifest — when CKAN is unreachable, fall back to
data/seed_urls.jsonshipped in the wheel. The response is flaggedstale: truewith an honest reason.
Net effect: a fresh uvx wgea-mcp always gets the current reporting year; a 12-month-old install still works because the seed manifest is refreshed and --upgrade pulls a new wheel.
Fuzzy employer-name search
Pass any abbreviation, alias, or substring and rapidfuzz resolves it:
You type | Resolved to |
| Commonwealth Bank of Australia |
| Commonwealth Bank of Australia |
| National Australia Bank Limited |
| Westpac Banking Corporation |
| Woolworths Group Limited |
| Atlassian Pty Ltd |
| Qantas Airways Limited |
When nothing exact matches, did_you_mean carries the top-5 closest legal names so the agent can ask the user to pick.
Attribution
Data sourced from the Workplace Gender Equality Agency. Licensed under Creative Commons Attribution 3.0 Australia (CC BY 3.0 AU). wgea-mcp is MIT-licensed; WGEA's data carries the upstream CC-BY 3.0 AU licence, echoed in every response's attribution field.
Per-employer reporting is a deliberate disclosure under the Workplace Gender Equality Act 2012 — redistribution is explicitly intended.
Sister MCPs (Australian Public Data portfolio)
Want all 9 sources behind one REST API? The hosted gateway at ausdata.io adds cross-source joins, full history, webhooks, and HMAC-signed responses on top of these MCPs — free tier (500 calls/mo, no card).
abs-mcp — Australian Bureau of Statistics (CPI, unemployment, ERP, building approvals)
rba-mcp — Reserve Bank of Australia (cash rate, lending stats, exchange rates)
ato-mcp — Australian Taxation Office (tax stats, ACNC charities)
apra-mcp — Australian Prudential Regulation Authority (banking, insurance, super)
aihw-mcp — Australian Institute of Health and Welfare
asic-mcp — Australian Securities and Investments Commission (company registers)
aemo-mcp — Australian Energy Market Operator (NEM dispatch, spot prices, generation)
au-weather-mcp — Open-Meteo (Bureau of Meteorology aggregator)
wgea-mcp — this one. Workplace gender equality.
aus-identity — Postcode / state / ABN normalisation helper used by all sisters
Development
git clone https://github.com/Bigred97/wgea-mcp.git
cd wgea-mcp
uv venv
uv pip install -e ".[dev]"
pytest # unit tests
pytest -m live # integration tests against data.gov.au (downloads the ~71 MB ZIP)Issues and contributions welcome: github.com/Bigred97/wgea-mcp/issues.
Available Tools
6 toolsdescribe_datasetA
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.
ParametersJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| dataset_id | Yes | Curated dataset ID. Use the search endpoint or search tool to discover, or the list-curated endpoint/tool to enumerate. Case-insensitive. |
Output Schema
ParametersJSON Schema
| Name | Required | Description |
|---|---|---|
| id | Yes | |
| name | Yes | |
| measures | No | |
| dimensions | No | |
| is_curated | Yes | |
| source_url | Yes | |
| description | Yes | |
| download_url | No | |
| period_coverage | No | |
| update_frequency | No | |
| reporting_year_latest | No |
TDQS
A4.5/5.0
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations exist, so description carries full burden. It implicitly discloses read-only behavior by stating it describes and returns metadata, but does not explicitly confirm no side effects. The return structure is detailed, adding transparency.
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 two-paragraph structure with front-loaded purpose and immediate usage guidance. Every sentence adds value; no wasted words.
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?
Complete description for a simple tool. Covers purpose, usage, return structure, and links to sibling tools. Output schema exists, so detailed field descriptions are not needed in the description.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% with one parameter (dataset_id) documented with examples and description. Description adds usage context but no additional semantic detail for the parameter beyond the schema.
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?
Clearly states it describes a dataset's filterable dimensions, measures, units, and source. Includes concrete examples of filter keys and measure aliases, distinguishing it from siblings like get_data.
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?
Explicitly instructs to use before calling get_data on a new dataset, providing clear when-to-use context and indicating it's a preparatory step.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_dataA
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.
ParametersJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| format | No | Response shape. 'records' (default): flat list of observations. 'series': grouped by measure. 'csv': pandas CSV string in `csv` field. | records |
| filters | No | Dimension filters. Keys are plain-English aliases from the dataset's describe_dataset response. Values are matched against the source data; pass a list to OR across values. Permissive dimensions (e.g. employer_name, question_text) accept any string and support fuzzy matching — try {'employer_name': 'CBA'} or {'employer_name': 'commonwealth*'} for wildcard substring search. | |
| max_rows | No | Cap on returned rows after filtering. Default 2000. Max 10000. Tighten filters to narrow further. | |
| dataset_id | Yes | Curated dataset ID. Use the search or list-curated endpoint/tool to discover. | |
| end_period | No | Inclusive end reporting year. Same format as start_period. | |
| start_period | No | Inclusive start reporting year. Format: 'YYYY-YY' (e.g. '2023-24') or 'YYYY' (matched against WGEA's reporting_year column). Bare int years like 2023 are coerced to '2023' automatically. |
Output Schema
ParametersJSON Schema
| Name | Required | Description |
|---|---|---|
| csv | No | |
| unit | No | |
| query | No | |
| stale | No | |
| period | No | Canonical 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. |
| source | No | |
| records | No | |
| row_count | No | |
| dataset_id | Yes | |
| source_url | Yes | |
| attribution | No | |
| dataset_name | Yes | |
| did_you_mean | No | |
| download_url | No | |
| retrieved_at | Yes | |
| stale_reason | No | |
| truncated_at | No | |
| reporting_year | No | |
| server_version | No |
TDQS
A4.2/5.0
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 of behavioral disclosure. It transparently describes the return structure, including fields like row_count, download_url, and fuzzy hints. It also mentions fuzzy matching on employer_name and wildcard support. However, it does not discuss error handling, rate limits, or idempotency.
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-structured with separate sections for examples and return information. It is front-loaded with the core purpose. While it is somewhat lengthy, every sentence serves a purpose, and the organization helps readability without sacrificing clarity.
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 tool's complexity (6 parameters, no annotations, but with output schema), the description provides sufficient context. It covers practical usage through examples, parameter semantics, and return details. The agent can effectively decide when and how to invoke this tool without ambiguous gaps.
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?
Input schema has 100% coverage, establishing a baseline of 3. The description adds value beyond schema by providing detailed examples for filters and start_period format, explaining fuzzy matching, and clarifying the default and maximum for max_rows. This enriches the semantic understanding for the agent.
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's function: 'Query a curated WGEA dataset and return observations.' It uses specific verbs and resources, and provides multiple concrete examples covering different datasets and filters, leaving no ambiguity about what the tool does.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description includes examples showing typical usage contexts but does not explicitly state when to use this tool versus alternatives like search_datasets or describe_dataset. There is no guidance on when not to use this tool or when a sibling would be more appropriate.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
latestA
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).
ParametersJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Cap on returned rows (portfolio-standard name). Default 2000, max 10000. Mutually exclusive with the legacy `max_rows` alias — supplying both raises ValueError. | |
| filters | No | Same filter shape as get_data. Useful for narrowing to one employer. | |
| max_rows | No | Legacy 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_id | Yes | Curated dataset ID. |
Output Schema
ParametersJSON Schema
| Name | Required | Description |
|---|---|---|
| csv | No | |
| unit | No | |
| query | No | |
| stale | No | |
| period | No | Canonical 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. |
| source | No | |
| records | No | |
| row_count | No | |
| dataset_id | Yes | |
| source_url | Yes | |
| attribution | No | |
| dataset_name | Yes | |
| did_you_mean | No | |
| download_url | No | |
| retrieved_at | Yes | |
| stale_reason | No | |
| truncated_at | No | |
| reporting_year | No | |
| server_version | No |
TDQS
A4.2/5.0
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.
list_curatedA
List every curated dataset ID in this version of wgea-mcp.
Returns: Sorted list of dataset IDs.
ParametersJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Output Schema
ParametersJSON Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
TDQS
A4.3/5.0
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description mentions the sorted return order, which adds behavioral context beyond the schema. No annotations exist, so the description carries the burden; it communicates that the tool is a read-only listing with no side effects, sufficiently transparent for a no-parameter tool.
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 two clear sentences: first states the action, second describes the return. Every word is necessary and front-loaded, with no superfluous content.
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 tool has no parameters and a simple output (sorted list of IDs), the description fully covers its purpose and return. The presence of an output schema is mentioned in context, but the description already sufficiently explains the return format.
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?
With zero parameters and 100% schema coverage, the baseline is 4. The description adds no additional parameter semantics, which is acceptable as there are no parameters to document.
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 lists every curated dataset ID, specifying the version context. It distinguishes itself from sibling tools like search_datasets (which likely filters) and top_n (which limits) by emphasizing 'every' and 'sorted', making its purpose unambiguous.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No explicit usage guidance or differentiation from sibling tools (e.g., when to use list_curated vs search_datasets). The description is adequate for a simple listing tool but lacks explicit alternatives or when-not scenarios, which are present in sibling tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
search_datasetsA
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.
ParametersJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Maximum number of results to return, ranked by relevance. | |
| query | Yes | Free-text search query. Matches against dataset IDs, names, descriptions, and curated search keywords. Case-insensitive. |
Output Schema
ParametersJSON Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
TDQS
A4/5.0
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It discloses the fuzzy-search behavior, output fields (id, name, description, update_frequency, is_curated), and that results are ranked. It does not mention rate limits or authentication but given the read-only nature, the transparency is adequate.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness3/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is structured with paragraphs and examples, but the list of seven datasets could be moved to a separate reference. It is informative but slightly verbose, earning a score of 3.
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?
With an output schema implied (DatasetSummary fields mentioned), the description covers return values and ranking. It lacks details on pagination or error handling, but given the tool's simplicity, this is sufficient.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% description coverage, so the schema already documents both parameters. The description adds context about the dataset catalog content but does not significantly enhance parameter meaning beyond what the schema provides.
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 is a fuzzy-search tool for the curated WGEA dataset catalog, specifying the verb and resource. It lists the seven curated datasets, distinguishing it from siblings like list_curated, which likely lists all datasets without search.
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 illustrate typical use cases (e.g., 'parental leave', 'women in management'), and results are described as ranked by relevance. While it does not explicitly state when not to use it, the context with sibling tools implies that list_curated is for browsing all datasets and describe_dataset for details on a specific one.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
top_nA
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.
ParametersJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| n | No | How many top (or bottom) rows to return. | |
| filters | No | Optional dimension filters, same shape as get_data. | |
| measure | Yes | Numeric measure column to rank by. WGEA measures are `n_employees` (WORKFORCE_COMPOSITION, WORKFORCE_MANAGEMENT) or `n_responses` (the other five questionnaire datasets). Use the describe endpoint or describe tool to confirm. | |
| direction | No | 'top' returns the N rows with the LARGEST measure values (highest n_employees, biggest n_responses, etc.). 'bottom' returns the SMALLEST. | top |
| dataset_id | Yes | Curated dataset ID. Use the search or list-curated endpoint/tool to discover. | |
| reporting_year | No | Optional single WGEA reporting year to restrict the ranking to. Format: 'YYYY-YY' (e.g. '2024-25') or 'YYYY' (e.g. '2024'). Defaults to the latest reporting year present in the data so the rank is a clean 'top N at the current reporting year' view. |
Output Schema
ParametersJSON Schema
| Name | Required | Description |
|---|---|---|
| csv | No | |
| unit | No | |
| query | No | |
| stale | No | |
| period | No | Canonical 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. |
| source | No | |
| records | No | |
| row_count | No | |
| dataset_id | Yes | |
| source_url | Yes | |
| attribution | No | |
| dataset_name | Yes | |
| did_you_mean | No | |
| download_url | No | |
| retrieved_at | Yes | |
| stale_reason | No | |
| truncated_at | No | |
| reporting_year | No | |
| server_version | No |
TDQS
A4.3/5.0
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description carries full burden. It discloses ranking across one reporting year, default behavior, sorting, and return format. No contradictions. Could mention read-only nature but implied.
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?
Well-structured with purpose, context, examples, and return info. Examples are helpful but make it slightly longer. Still clear and front-loaded.
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 6 parameters and output schema exists, description covers concept, parameters, examples, and return. Could mention error handling but complete enough for typical use.
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?
Input schema covers 100% of parameters with descriptions. Description adds value by explaining measure meaning per dataset, reporting year format, and providing examples that illustrate usage.
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 the N rows with largest/smallest measure value. It distinguishes from sibling tools like get_data by explaining server-side rank-and-slice, and provides concrete examples.
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 indicates this is the most common agent workflow for top N queries and contrasts with fetching full tables. It defaults to latest year but doesn't explicitly state when not to use it, though the context is clear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.
6 tool updates
v0.6.11- Added
describe_dataset - Added
get_data - Added
latest - Added
list_curated - Added
search_datasets - Added
top_n
6 tool updates
v0.6.7- Removed
describe_dataset - Removed
get_data - Removed
latest - Removed
list_curated - Removed
search_datasets - Removed
top_n
3 tool updates
v0.6.4- Changed
describe_dataset1 field changed- changed
Input schema / properties / dataset_id / descriptionPrevious value: -"Curated dataset ID. Use search_datasets() to discover or list_curated() to enumerate. Case-insensitive."New value: +"Curated dataset ID. Use the search endpoint or search tool to discover, or the list-curated endpoint/tool to enumerate. Case-insensitive."
- Changed
get_data1 field changed- changed
Input schema / properties / dataset_id / descriptionPrevious value: -"Curated dataset ID. Use search_datasets() / list_curated()."New value: +"Curated dataset ID. Use the search or list-curated endpoint/tool to discover."
- Changed
top_n2 fields changed- changed
Input schema / properties / dataset_id / descriptionPrevious value: -"Curated dataset ID. Use search_datasets() / list_curated()."New value: +"Curated dataset ID. Use the search or list-curated endpoint/tool to discover." - changed
Input schema / properties / measure / descriptionPrevious value: -"Numeric measure column to rank by. WGEA measures are `n_employees` (WORKFORCE_COMPOSITION, WORKFORCE_MANAGEMENT) or `n_responses` (the other five questionnaire datasets). Use describe_dataset() to confirm."New value: +"Numeric measure column to rank by. WGEA measures are `n_employees` (WORKFORCE_COMPOSITION, WORKFORCE_MANAGEMENT) or `n_responses` (the other five questionnaire datasets). Use the describe endpoint or describe tool to confirm."
1 tool update
v0.5.0- Changed
latest2 fields changed- added
Input schema / properties / limitAdded value: +{ + "anyOf": [ + { + "maximum": 10000, + "minimum": 1, + "type": "integer" + }, + { + "type": "null" + } + ], + "default": null, + "description": "Cap on returned rows (portfolio-standard name). Default 2000, max 10000. Mutually exclusive with the legacy `max_rows` alias — supplying both raises ValueError.", + "examples": [ + 100, + 500, + 2000 + ] +} - changed
Input schema / properties / max_rows / descriptionPrevious value: -"Cap on returned rows. Default 2000, max 10000."New value: +"Legacy 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."
1 tool update
v0.4.0- Added
top_n
2 tool updates
v0.3.0- Changed
get_data6 fields changed- changed
Input schema / properties / end_period / anyOfPrevious value: -[ - { - "type": "string" - }, - { - "type": "null" - } -]New value: +[ + { + "type": "string" + }, + { + "type": "integer" + }, + { + "type": "null" + } +] - changed
Input schema / properties / end_period / examplesPrevious value: -[ - "2024-25", - "2025-26" -]New value: +[ + "2024-25", + "2025-26", + 2024 +] - changed
Input schema / properties / start_period / anyOfPrevious value: -[ - { - "type": "string" - }, - { - "type": "null" - } -]New value: +[ + { + "type": "string" + }, + { + "type": "integer" + }, + { + "type": "null" + } +] - changed
Input schema / properties / start_period / descriptionPrevious value: -"Inclusive start reporting year. Format: 'YYYY-YY' (e.g. '2023-24') or 'YYYY' (matched against WGEA's reporting_year column)."New value: +"Inclusive start reporting year. Format: 'YYYY-YY' (e.g. '2023-24') or 'YYYY' (matched against WGEA's reporting_year column). Bare int years like 2023 are coerced to '2023' automatically." - changed
Input schema / properties / start_period / examplesPrevious value: -[ - "2023-24", - "2024-25", - "2023" -]New value: +[ + "2023-24", + "2024-25", + "2023", + 2023 +] - added
Output schema / properties / periodAdded value: +{ + "additionalProperties": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ] + }, + "description": "Canonical 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.", + "type": "object" +}
- Changed
latest1 field changed- added
Output schema / properties / periodAdded value: +{ + "additionalProperties": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ] + }, + "description": "Canonical 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.", + "type": "object" +}
1 tool update
v0.1.5- Changed
latest1 field changed- added
Input schema / properties / max_rows / examplesAdded value: +[ + 100, + 500, + 2000 +]
2 tool updates
v0.1.4- Changed
get_data1 field changed- added
Output schema / properties / truncated_atAdded value: +{ + "anyOf": [ + { + "type": "integer" + }, + { + "type": "null" + } + ], + "default": null +}
- Changed
latest1 field changed- added
Output schema / properties / truncated_atAdded value: +{ + "anyOf": [ + { + "type": "integer" + }, + { + "type": "null" + } + ], + "default": null +}
5 tool updates
v0.1.2- First observed
describe_dataset - First observed
get_data - First observed
latest - First observed
list_curated - First observed
search_datasets
TDQS
A4.4/5.0
Disambiguation5/5
Each tool has a clearly distinct purpose: describe_dataset for metadata, get_data for raw queries, latest for latest year convenience, list_curated for dataset inventory, search_datasets for discovery, and top_n for ranked results. No functional overlap.
Naming Consistency4/5
Most tools follow verb_noun snake_case (describe_dataset, get_data, list_curated, search_datasets). 'latest' and 'top_n' deviate slightly but are predictable and in line with common conventions for convenience tools.
Tool Count5/5
Six tools is well-scoped for a specialized dataset query server. Each tool addresses a distinct workflow step: discovery, metadata inspection, data retrieval, latest-year convenience, and top-N ranking.
Completeness5/5
The tool set covers the full exploration-to-query lifecycle: list available datasets, search, describe, query with filters, get latest year, and get top/bottom rows. No obvious gaps for a read-only public data API.
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