AusEcon MCP for ABS | RBA | APRA data
Related Servers
Alternatives to AusEcon MCP for ABS | RBA | APRA data
- FlicenseCqualityCmaintenanceProvides access to the Australian Bureau of Statistics (ABS) Data API. This server allows AI assistants to query and analyze ABS statistical data.19-
- AlicenseAqualityBmaintenanceOne-call Australian economic data plumbing via the RBA — cited responses for rates and macro series, not a data broker.678 PyPIMIT
Related Servers
- AlicenseAqualityFmaintenanceCited Australian stats via the ausdata.io gateway — stable AU.* series IDs, source_url + retrieved_at on every response. Free tier. Not a data broker; upgrade for Embed / signed / webhooks.2832 npm2MIT
- AlicenseAqualityCmaintenanceEnables MCP clients to query Australian economic and transport data from ABS, RBA, and Sydney Opal tap sources, including inflation, labour force, interest rates, exchange rates, lending rates, and commute patterns.1030 npmMIT
- AlicenseAqualityDmaintenanceA local MCP server for public Reserve Bank of Australia data, exposing six read-only tools backed by the R package readrba.6MIT
- AlicenseAqualityFmaintenanceOne-call Australian energy-market plumbing via AEMO — cited, structured responses for market data and analysis, not a data broker.581 PyPIMIT
- AlicenseNot gradedqualityBmaintenanceMCP server providing access to Statistics Canada time-series data via web data.30 npmMIT
- AlicenseAqualityAmaintenanceMCP server for the Brazilian Central Bank (BCB/SGS) public API, providing access to 18,000+ economic time series. Includes a curated catalog of 150+ popular indicators organized in 12 categories: interest rates (Selic), inflation (IPCA, IGP-M, INPC), exchange rates (USD, EUR), GDP, employment, credit, fiscal data, and more. Supports historical queries with date filters, latest values, metadata loo17214 npm10MIT
TDQS
Scored across 14 tools
There are multiple overlapping discovery and retrieval tools: search_datasets vs list_catalogue, get_economic_series vs get_latest_observations/get_top_observations, and describe_dataset vs get_abs_dataset_structure. The descriptions do help clarify roles, but an agent could still hesitate between source-native and analyst-facing retrieval paths.
All tool names follow a consistent snake_case verb_noun pattern using search_, list_, get_, and describe_. Source-specific retrieval names like get_abs_data, get_rba_table, and get_apra_data follow the same predictable structure.
14 tools is within the reasonable range for a multi-source economic data server, but the deprecated list_rba_tables alias and the two convenience wrappers add slight redundancy. The count is not excessive, just a little larger than necessary.
The tool surface covers discovery, catalogue browsing, concept lookup, dataset structure, source-native retrieval across ABS, RBA, and APRA, analyst-facing series retrieval, derived series, and release events. For a read-only data server, this is comprehensive with no obvious dead ends.