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Built for the Claude Code community with Claude Code by @mertduzgun

Independent project, not affiliated with Anthropic

Australian Economic Data (RBA & ABS)

anthonypuggs/ausecon-mcp-server
210 toolsSTDIOregistry active
Summary

Connects Claude directly to the Australian Bureau of Statistics, Reserve Bank of Australia, and APRA data feeds. You get ten tools for querying CPI, GDP, cash rate targets, labour force stats, and 70 other curated economic series, plus nine derived indicators like real cash rate calculations. The semantic layer means you can ask for concepts like "cash_rate_target" instead of hunting through dataset codes, though raw ABS and RBA table access is available when you need exact source control. Built on stdio with HTTP transport support, ships via PyPI, and includes prompt templates for common analyst workflows. Useful when you're building economic analysis tools or need Australian macro data without writing yet another scraper.

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Tools

Public tool metadata for what this MCP can expose to an agent.

10 tools
search_datasetsSearch curated ABS, RBA, and APRA economic datasets.2 params

Search curated ABS, RBA, and APRA economic datasets.

Parameters* required
querystring
Discovery query text.
sourcevalue
Optional source filter. Use abs for Australian Bureau of Statistics, rba for Reserve Bank of Australia, or apra for Australian Prudential Regulation Authority.
list_catalogueList curated ABS, RBA, and APRA catalogue entries, optionally filtered by source, category, or tag. Unranked complement to ``search_datasets``.5 params

List curated ABS, RBA, and APRA catalogue entries, optionally filtered by source, category, or tag. Unranked complement to ``search_datasets``.

Parameters* required
tagvalue
Optional curated catalogue tag filter.
sourcevalue
Optional source filter. Use abs for Australian Bureau of Statistics, rba for Reserve Bank of Australia, or apra for Australian Prudential Regulation Authority.
categoryvalue
Optional curated catalogue or semantic concept category filter.
include_ceasedboolean
Whether to include ceased ABS catalogue entries.default: false
include_discontinuedboolean
Whether to include discontinued RBA catalogue entries.default: false
list_economic_conceptsList analyst-friendly semantic economic concepts accepted by get_economic_series.3 params

List analyst-friendly semantic economic concepts accepted by get_economic_series.

Parameters* required
queryvalue
Optional query for filtering semantic economic concepts.
sourcevalue
Optional source filter. Use abs for Australian Bureau of Statistics, rba for Reserve Bank of Australia, or apra for Australian Prudential Regulation Authority.
categoryvalue
Optional curated catalogue or semantic concept category filter.
get_abs_dataset_structureGet ABS SDMX dataset dimensions and codelists.1 params

Get ABS SDMX dataset dimensions and codelists.

Parameters* required
dataflow_idstring
Non-empty dataset or table id.
get_abs_dataExpert/source-native ABS SDMX retrieval in a normalised response shape.6 params

Expert/source-native ABS SDMX retrieval in a normalised response shape.

Parameters* required
keystring
ABS SDMX key, or "all" for all series.default: all
last_nvalue
Optional positive observation count limit.
end_periodvalue
Optional ABS period bound in YYYY, YYYY-QN, YYYY-MM, or YYYY-SN format.
dataflow_idstring
Non-empty dataset or table id.
start_periodvalue
Optional ABS period bound in YYYY, YYYY-QN, YYYY-MM, or YYYY-SN format.
updated_aftervalue
Optional ISO date or datetime accepted by the ABS updatedAfter API.
list_rba_tablesDeprecated compatibility alias. Prefer list_catalogue(source="rba").2 params

Deprecated compatibility alias. Prefer list_catalogue(source="rba").

Parameters* required
categoryvalue
Optional RBA catalogue category filter.
include_discontinuedboolean
Whether to include discontinued RBA catalogue entries.default: false
get_rba_tableExpert/source-native RBA statistical table retrieval in a normalised response shape.5 params

Expert/source-native RBA statistical table retrieval in a normalised response shape.

Parameters* required
last_nvalue
Optional positive observation count limit.
end_datevalue
Optional ISO date bound in YYYY-MM-DD format.
table_idstring
Non-empty dataset or table id.
series_idsvalue
Optional list of non-empty source-native series IDs to keep after download.
start_datevalue
Optional ISO date bound in YYYY-MM-DD format.
get_apra_dataExpert/source-native APRA public XLSX publication retrieval. Only curated official APRA publication IDs are accepted; arbitrary URLs are not.6 params

Expert/source-native APRA public XLSX publication retrieval. Only curated official APRA publication IDs are accepted; arbitrary URLs are not.

Parameters* required
last_nvalue
Optional positive observation count limit.
end_datevalue
Optional ISO date bound in YYYY-MM-DD format.
table_idvalue
Non-empty dataset or table id.
series_idsvalue
Optional list of non-empty source-native series IDs to keep after download.
start_datevalue
Optional ISO date bound in YYYY-MM-DD format.
publication_idstring
Non-empty dataset or table id.
get_economic_seriesPreferred analyst-facing retrieval tool for curated ABS/RBA economic concepts. Use list_economic_concepts for discovery. Date bounds accept YYYY, YYYY-QN, YYYY-SN, YYYY-MM, or YYYY-MM-DD and are normalised to the resolved source.7 params

Preferred analyst-facing retrieval tool for curated ABS/RBA economic concepts. Use list_economic_concepts for discovery. Date bounds accept YYYY, YYYY-QN, YYYY-SN, YYYY-MM, or YYYY-MM-DD and are normalised to the resolved source.

Parameters* required
endvalue
Optional analyst-friendly date bound: YYYY, YYYY-QN, YYYY-SN, YYYY-MM, or YYYY-MM-DD. Semantic retrieval normalises this to the resolved source frequency.
startvalue
Optional analyst-friendly date bound: YYYY, YYYY-QN, YYYY-SN, YYYY-MM, or YYYY-MM-DD. Semantic retrieval normalises this to the resolved source frequency.
last_nvalue
Optional positive observation count limit.
conceptstring
Curated semantic concept name.
variantvalue
Optional curated concept variant, such as headline, underlying, or target.
frequencyvalue
Optional requested frequency for a curated concept, such as monthly, quarterly, or annual.
geographyvalue
Optional geography selector for a curated concept, usually aus for Australia.
get_derived_seriesRead-only retrieval for the narrow transparent derived series layer. Date bounds accept YYYY, YYYY-QN, YYYY-SN, YYYY-MM, or YYYY-MM-DD and are normalised to the derived series frequency.4 params

Read-only retrieval for the narrow transparent derived series layer. Date bounds accept YYYY, YYYY-QN, YYYY-SN, YYYY-MM, or YYYY-MM-DD and are normalised to the derived series frequency.

Parameters* required
endvalue
Optional analyst-friendly bound for a derived series: YYYY, YYYY-QN, YYYY-SN, YYYY-MM, or YYYY-MM-DD. Bounds are normalised to the derived series frequency.
startvalue
Optional analyst-friendly bound for a derived series: YYYY, YYYY-QN, YYYY-SN, YYYY-MM, or YYYY-MM-DD. Bounds are normalised to the derived series frequency.
last_nvalue
Optional positive observation count limit.
conceptstring
Derived concept name.
ausecon — Australian economic & financial data MCP server

CI Integration PyPI Python Transport License smithery badge

ausecon-mcp-server MCP server

ausecon is a Model Context Protocol server that gives any AI assistant clean, structured access to Australia’s core economic and financial data — straight from the ABS, RBA, and APRA.

Open · free · no API key — always fresh & fully source-traceable, three official sources in one shape.

Documentation  ·  Getting started  ·  Tool reference  ·  Changelog


Why this exists

Australian economic data is authoritative but awkward to reach — scattered across portals, formats, and identifiers you have to memorise. ausecon is the open, free, no-API-key way to put it in front of any AI assistant: every series comes back fresh and fully source-traceable, stamped with its provenance, in one consistent shape (metadata · series · observations) across all three regulators. Ask for “the cash rate” or “quarterly real GDP growth” and get research-grade data back without leaving the conversation.

14
read-only tools
81
economic concepts
16
derived indicators
8
prompt templates
3
data sources

What you get

🔒 Fresh & source-traceable

Every value is fetched live and stamped with its provenance (retrieved_at, source, server_version). On an upstream outage, a cached fallback is always flagged stale — never served silently.

🧱 Three sources, one shape

ABS, RBA and APRA all return the same tidy metadata · series · observations structure.

🧮 Transparent derived series

Formula-based indicators like real_cash_rate — every calculation is open and inspectable.

🎯 Source-native control

Drop down to raw get_abs_data, get_rba_table or get_apra_data whenever you need exact control.

⚡ Quick-turn helpers

Convenience tools for latest observations, top movers and release events — analysis in one call.

🔌 Plugs into your client

Claude Desktop, Claude Code, Cursor, Windsurf, VS Code, Codex or Smithery. stdio locally, Streamable HTTP when hosted.

Data sources

SourceCoverage
ABS  ·  Australian Bureau of StatisticsNational accounts, prices, labour force, population
RBA  ·  Reserve Bank of AustraliaCash rate, monetary & financial aggregates, exchange rates
APRA  ·  Aust. Prudential Regulation AuthorityADI & insurer statistics, with release-cadence estimates

Try it instantly (no install)

Prefer not to install anything? A hosted, read-only, no-API-key instance speaks MCP over Streamable HTTP at:

https://ausecon-mcp-server.onrender.com/mcp

Point any MCP client that supports remote (Streamable HTTP) servers at that URL — for example, in Claude Code:

claude mcp add --transport http ausecon https://ausecon-mcp-server.onrender.com/mcp

The hosted instance may take a few seconds to wake on the first request.

Install

The package lives on PyPI and is designed to be launched on demand by your MCP client via uvx:

uvx ausecon-mcp-server

The server speaks MCP over standard input/output. Launched on its own, it simply waits for a client to connect.

Connect your client

Claude Code
claude mcp add --transport stdio ausecon -- uvx ausecon-mcp-server
Codex
codex mcp add ausecon -- uvx ausecon-mcp-server
Claude Desktop

Add to your claude_desktop_config.json:

{
  "mcpServers": {
    "ausecon": {
      "command": "uvx",
      "args": ["ausecon-mcp-server"]
    }
  }
}
Cursor

Add to ~/.cursor/mcp.json (global) or .cursor/mcp.json (project):

{
  "mcpServers": {
    "ausecon": {
      "command": "uvx",
      "args": ["ausecon-mcp-server"]
    }
  }
}

Or paste this one-click link into your browser:

cursor://anysphere.cursor-deeplink/mcp/install?name=ausecon&config=eyJjb21tYW5kIjoidXZ4IiwiYXJncyI6WyJhdXNlY29uLW1jcC1zZXJ2ZXIiXX0=
Windsurf

Add to ~/.codeium/windsurf/mcp_config.json:

{
  "mcpServers": {
    "ausecon": {
      "command": "uvx",
      "args": ["ausecon-mcp-server"],
      "env": {}
    }
  }
}
VS Code

Install in VS Code

Or add to .vscode/mcp.json (workspace) or your user mcp.json:

{
  "servers": {
    "ausecon": {
      "type": "stdio",
      "command": "uvx",
      "args": ["ausecon-mcp-server"]
    }
  }
}

Hosting it instead? smithery.yaml and Dockerfile.smithery ship a Streamable HTTP deployment at /mcp. See the Smithery guide.

A quick taste

Find the concept you want, then ask for the series:

list_economic_concepts(query="cash rate")

get_economic_series(
    concept="cash_rate_target",
    start="2020-01-01",
)

Need a transparent, formula-based indicator? Call the derived surface directly:

get_derived_series(concept="real_cash_rate", last_n=12)

Connected to an AI agent, you can skip the syntax entirely — ask for “quarterly real GDP growth” and it maps your request to the right tool calls for you.

Develop locally

Python 3.12 is recommended; the CI matrix supports 3.10+.

uv sync --python 3.12 --extra dev
uv run pytest
uv run ruff check src tests scripts

auseconmcp.com  ·  Issues  ·  MIT Licence  ·  Made for the Australian data community

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Categories
Data & Analytics
Registryactive
Packageausecon-mcp-server
TransportSTDIO
UpdatedApr 19, 2026
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