Aihw MCP Server

aihw-mcp

mcp-name: io.ausdata/aihw-mcp

MCP server for Australian Institute of Health and Welfare statistics. Plain-English access to long-term mortality (GRIM), regional mortality (MORT), cancer incidence and mortality (ACIM), national health expenditure, youth justice detention, and the public hospitals register — all 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).

"How have diabetes deaths changed since 1980?"
"What's the age-standardised mortality rate in the Sydney - Inner West SA3?"
"Show me breast cancer incidence in women aged 50–54 over time."
"How much did Australia spend on public hospitals in 2022-23?"
"How many young people are in detention in NSW vs VIC?"
"List all Principal referral hospitals in Queensland."
"Top 5 causes of death in 2023."

Sister to abs-mcp (Australian Bureau of Statistics), rba-mcp (Reserve Bank of Australia), ato-mcp (Australian Taxation Office), and au-weather-mcp (Australian weather). The five together cover the macro / regulator / tax / health / climate layer of Australian official data.

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Install

# Run on demand via uvx (recommended)
uvx --upgrade aihw-mcp

# Or install permanently
pip install aihw-mcp

Claude Desktop

Add to claude_desktop_config.json:

{
  "mcpServers": {
    "aihw": { "command": "uvx", "args": ["--upgrade", "aihw-mcp"] }
  }
}

Why --upgrade? uvx aihw-mcp (without the flag) uses whatever wheel is cached and never adopts new PyPI releases on its own. --upgrade makes uvx check PyPI on each launch and pull a newer release if one exists. To verify which version is currently serving you, look at the server_version field on any DataResponse.

Claude Code / Cursor

claude mcp add aihw --command uvx --args -- --upgrade aihw-mcp

Auto-updating data

Beyond the wheel-level --upgrade, the server has a second auto-update path inside the data layer: when AIHW refreshes GRIM with another year of deaths data or publishes a new MORT release, aihw-mcp resolves the new resource URL via data.gov.au's CKAN API at fetch time and uses the freshest match. Hard-coded YAML URLs are the safe fallback if discovery fails. You do not need to wait for a new wheel release to get new yearly data — just delete ~/.aihw-mcp/cache.db to force a refresh, or wait for the 7-day TTL to expire.

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What it exposes

Six tools, all plain-English in, structured out:

Every response is the same shape — dataset_id, dataset_name, query, period, unit, row_count, records, aihw_url, attribution, server_version — across every curated dataset.

Time-series datasets (GRIM, MORT, ACIM, Health Expenditure, Youth Justice) accept start_period and end_period on get_data — e.g. start_period="2000", end_period="2010" narrows GRIM to that decade. latest() returns the most-recent observation per measure, sorted by the dataset's declared period dimension (not by source row order). Error messages include fuzzy "did you mean?" suggestions when you typo a filter or measure name.

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Curated datasets (6 in v0.1)

Adding a new dataset is a single YAML drop into src/aihw_mcp/data/curated/ — see CONTRIBUTING.md.

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Example queries (paste into Claude)

Cross-source compatibility. The state filter on HEALTH_EXPENDITURE, PUBLIC_HOSPITALS, YOUTH_JUSTICE_DETENTION, and other location-aware datasets accepts canonical state codes ("NSW"), full names ("New South Wales"), case-insensitive variants ("nsw"), ISO 3166-2 ("AU-NSW"), and 4-digit postcodes ("2000" → NSW). Powered by aus-identity — the same input format works across abs-mcp, ato-mcp, apra-mcp, aihw-mcp, and asic-mcp.

Public-health research: "For GRIM_DEATHS, give me the deaths and age-standardised rate for 'Diabetes' for Persons every year from 1980 to the latest, so I can chart the trajectory."

Health-tech / regional analysis: "Using MORT_GEOGRAPHY, list the 10 SA3 regions with the highest age-standardised mortality rate for Persons in the most recent year."

Oncology: "From CANCER_INCIDENCE_MORTALITY, give me Breast cancer incidence in Females across the 50–54 age band for every available year, plus the same age band's mortality."

Health-policy: "From HEALTH_EXPENDITURE, what was the real spend on 'Public hospitals' in NSW in 2022-23, broken down by broad source (Government vs Non-government)?"

Criminal-justice tech: "Using YOUTH_JUSTICE_DETENTION, compare the average nightly youth-detention population in NSW vs VIC in 'Jun qtr 2017', for both Indigenous and Total."

Hospital-tech / market intel: "From PUBLIC_HOSPITALS, list every 'Principal referral' hospital with their state and Local Hospital Network. Then count how many there are per state."

Each prompt resolves to one or two get_data / top_n calls. The response includes the source URL so the agent can cite it back.

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Architecture

Same shape as the sister packages — client → cache → parsing → shaping → server:

• client.py wraps httpx with a SQLite-backed disk cache (per-resource TTL).
• parsing.py reads CSV (via pandas) and XLSX (via openpyxl/pandas). Header rows + sheet names live in the curated YAML so future format quirks are a YAML edit, not a code change.
• curated.py loads dataset specs from data/curated/*.yaml — each one declares its dimensions, measures, dimension value enums, source/download URLs, format, and parse layout.
• shaping.py transforms the parsed DataFrame into DataResponse (records / series / csv).
• server.py is the FastMCP entrypoint — six tools, full input validation with helpful "Try X" hints on error.

Cache lives under ~/.aihw-mcp/cache.db. Most AIHW datasets refresh once a year; the TTLs are tuned for that cadence.

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Attribution

Data sourced from the Australian Institute of Health and Welfare (AIHW) via data.gov.au. Licensed under Creative Commons Attribution 3.0 Australia (CC BY 3.0 AU). The MCP server is MIT-licensed; the data carries the upstream CC-BY 3.0 AU licence, which is echoed in every response's attribution field.

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Sister MCPs (Australian Public Data portfolio)

• 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 — this one. National mortality, regional health, cancer, expenditure, youth justice, hospitals.
• 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 — Workplace Gender Equality Agency
• aus-identity — Postcode / state / ABN normalisation helper used by all sisters

The portfolio is designed to compose: an agent can ask for "unemployment + cash rate + median income + mortality + climate" for postcode 2000 and one shot fans out across multiple MCPs.

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Roadmap (next iterations)

• v0.2: MORT_TABLE_2 (deaths by leading cause × region); intercountry adoptions; AIHW mental-health data tables (XLSX path)
• v0.3: hosted version with x402 per-call paywall; programmatic SEO pages
• v0.4: listing on MCPay + Apify; paid tier for high-volume agent users

CHANGELOG tracks every release.

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Development

git clone https://github.com/Bigred97/aihw-mcp.git
cd aihw-mcp
uv venv
uv pip install -e ".[dev]"
pytest                  # unit tests, no network
pytest -m live          # integration tests against data.gov.au

Issues, ideas, and contributions welcome: github.com/Bigred97/aihw-mcp/issues.