Australian Economic Data (ABS, RBA & APRA)

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

Open source, free, and no API key. Every series comes back fresh & fully source-traceable, and all three sources share one response shape.

Documentation  ·  Getting started  ·  Tool reference  ·  Changelog

Why this exists

Australian economic data is authoritative but awkward to reach. It sits behind three different portals with three different formats, and each one expects you to already know its identifiers. ausecon puts that data in front of an AI assistant with nothing to sign up for and no key to manage. Every series is fetched from the source, cached for up to an hour, stamped with when it was actually retrieved, and returned in the same metadata · series · observations shape whether it came from the ABS, the RBA, or APRA. Ask for "the cash rate" or "quarterly real GDP growth" and the assistant works out the right call.

What you get

Data sources

Try it instantly (no install)

A hosted read-only, no-API-key instance speaks MCP over Streamable HTTP at:

https://mcp.auseconmcp.com/mcp

Point any MCP client that supports remote servers at that URL. In Claude Code:

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

The hosted instance may take a few seconds to wake on the first request. The previous https://ausecon-mcp-server.onrender.com/mcp URL continues to work and points at the same instance.

Install

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

uvx ausecon-mcp-server

The server speaks MCP over standard input and output. Run on its own it just sits there waiting for a client, so there is nothing to see until one connects.

Connect your client

claude mcp add --transport stdio ausecon -- uvx ausecon-mcp-server

codex mcp add ausecon -- uvx ausecon-mcp-server

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

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

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

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

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

To host it yourself, 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",
)

Derived indicators have their own tool:

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 works out which tools to call.

Develop locally

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

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

The repo also ships a manual benchmark (evals/) that measures the server's impact on model answers across 52 Australian-economics questions, comparing a bare model, web search, and the ausecon tools. Ground-truth resolution is free to check:

uv run --group evals python -m evals.run_eval --dry-run

A full run makes paid API calls, so read the evaluation harness guide before starting one.