ato-mcp 0.3.0

MCP server for Australian Taxation Office statistics. Plain-English access to personal tax by postcode, company tax by industry, corporate tax transparency, GST collections, super contributions, and the ACNC charity register.

ato-mcp

MCP server for Australian Taxation Office statistics. Plain-English access to personal tax by postcode, company tax by industry, corporate tax transparency for every $100M+ entity, super contributions by age, salary by occupation, monthly GST collections, and the live ACNC charity register — all from a single uvx command.

"What's the median taxable income in postcode 2000?"
"How much tax did BHP pay last year?"
"Which industries have the highest gross income?"
"How many Large charities are there in NSW?"
"What's the average super contribution for under-30s in the top tax bracket?"

Sister to abs-mcp (Australian Bureau of Statistics), rba-mcp (Reserve Bank of Australia), and au-weather-mcp (Australian weather via Open-Meteo + BOM). The four together cover the macro / regulator / tax / climate layer of Australian official data.

---

Install

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

# Or install permanently
pip install ato-mcp

Claude Desktop

Add to claude_desktop_config.json:

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

Why --upgrade? uvx ato-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 ato --command uvx --args -- --upgrade ato-mcp

Auto-updating data

Beyond the wheel-level --upgrade, the server has a second auto-update path inside the data layer: when ATO publishes Taxation Statistics 2023-24 next year, ato-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 ~/.ato-mcp/cache.db to force a refresh, or wait for the 7-day TTL to expire.

---

What it exposes

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

Tool	Purpose
search_datasets	Fuzzy-search the curated catalog by keyword
describe_dataset	List a dataset's filterable dimensions and returnable measures
get_data	Query with filters, measures, period range, output format
latest	Last observation per measure (shortcut)
top_n	Rank rows by a measure, return top (or bottom) N
stats	Aggregate stats (count, sum, mean, median, min, max, stddev) over a measure — optional group_by partitions before aggregating
list_curated	Enumerate the curated dataset IDs

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

---

Curated datasets (14)

ID	What it is	Period	Coverage
IND_POSTCODE	Personal tax stats by taxable status × state × SA4 × postcode (~5,200 postcodes)	2022-23	80+ measures
IND_POSTCODE_MEDIAN	Median & average taxable income by postcode, every year	2003-04 → 2022-23	21 yearly measures
COMPANY_INDUSTRY	Company tax by ANZSIC broad + fine industry	2022-23	216 industry cells
CORP_TRANSPARENCY	Entity-level tax disclosure for $100M+ corporations (name, ABN, income, tax)	2023-24	~4,200 entities
SUPER_CONTRIB_AGE	Super contributions by age × sex × taxable income bracket	2022-23	Employer/personal/other
ACNC_REGISTER	Live register of every Australian charity (ABN, size, jurisdiction, beneficiaries)	Current (weekly)	~60,000 entities
GST_MONTHLY	Monthly GST / WET / LCT collections (gross GST, input tax credits, net GST, etc.)	2020-07 → 2024-06	10 metrics × 48 months
ATO_OCCUPATION	Median/average income (taxable, salary/wage, total) by ANZSCO occupation × sex	2022-23	~1,200 jobs × 7 measures
SMSF_FUNDS	SMSF sector size — total funds, total members, total gross assets (trillion-$ sector)	2019-20 → 2024-25	3 metrics × 6 years
SBB_BENCHMARKS	Industry total-expense + COGS ratio bands by turnover bracket (~100 industries)	2023-24	12 measures × 100 industries
HELP_DEBT	HECS/HELP outstanding debt, indexation, compulsory + voluntary repayments annual	2005-06 → 2024-25	8 measures × 20 years
TAX_GAPS	ATO's tax gap estimates — how much tax is being missed each year by tax type	2016-17 onward	5 measures × 4 tax types × ~7 years
RND_INCENTIVE	R&D Tax Incentive transparency — every entity's R&D claim (name, ABN, $)	2022-23	~13,000 entities
ACNC_AIS_FINANCIALS	Per-charity financials from the ACNC Annual Information Statement (revenue, expenses, staff)	2023	~60,000 charities × 23 measures

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

---

Example queries (paste into Claude)

Property-tech: "For postcodes 2000, 2008, 2026, and 2031 in NSW, give me the median taxable income across every available year so I can compare trajectories."

Corporate tax: "Get the total income, taxable income, and tax payable for BHP IRON ORE (JIMBLEBAR) PTY LTD."

Industry analysis: "Which fine industry codes under 'C. Manufacturing' have the highest total income, and how many companies are in each?"

Charity/non-profit tech: "Find every charity in NSW with size 'Large' that operates_in_VIC = Y."

Retirement planning: "What's the average personal super contribution for males aged 30-39 in the $120,001–$180,000 bracket?"

Each prompt resolves to one get_data call. The response includes the source URL so the agent can cite it back.

---

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 XLSX (via openpyxl/pandas) and CSV (via 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 — seven tools, full input validation with helpful "Try X" hints on error.

Cache lives under ~/.ato-mcp/cache.db. Data on data.gov.au refreshes once a year (ATO) or weekly (ACNC), and the TTLs are tuned for that.

---

Attribution

Data sourced from the Australian Taxation Office and the Australian Charities and Not-for-profits Commission, both 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.

---

Sister packages

• abs-mcp — ABS census and economic statistics (unemployment, CPI, GDP, population, building approvals)
• rba-mcp — RBA statistical tables (cash rate, FX rates, mortgage rates, money market)
• ato-mcp — this one. Tax, super, and charity registers.
• au-weather-mcp — Australian weather via Open-Meteo + BOM. 21 curated locations + postcode/place-name lookup, current observations, 16-day forecasts, 80yr historical archive.

All four are designed to compose: an agent can ask for "unemployment + cash rate + median income + climate" in postcode 2000 and one shot fans out across four MCPs.

---

Roadmap (next iterations)

• v0.2: GST_MONTHLY transposed time series; multi-year CORP_TRANSPARENCY; ATO_OCCUPATION (salary by occupation code)
• 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.

---

Development

git clone https://github.com/Bigred97/ato-mcp.git
cd ato-mcp
uv venv
uv pip install -e ".[dev]"
pytest                  # 53 unit tests, ~7s
pytest -m live          # 3 integration tests against data.gov.au, ~3s

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