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ABS Data (observed) MCP server

online · 100% uptime
by SamJB123·io.github.SamJB123/abs-data·v0.1.0
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Australian Bureau of Statistics data: 1,227 tables, offering only options confirmed to serve data

B76/100grade B
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B76/100

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ABS Data (observed) tools (6, 2 write)

write = sends, deletes, buys or posts
  • describe_tableFree

    A table's dimensions in key order, its coverage dates, and its observed options. Dimensions with up to 64 options list them inline with labels; larger ones (geography, occupations) say how many and are searchable with search_options.

  • executewrite actionFree

    Run JavaScript in an isolated sandbox whose only capability is `abs`, a client with the same four verbs as this server (searchTables, describeTable, searchOptions, getData) and the same guarantees: every selection is verified against ABS before fetching. Use it for multi-series or multi-table analysis — fetch several series, compute growth rates, rank capitals, join tables — and `return` only the computed result, so large payloads never reach the conversation. No network beyond `abs`. Budgets: 10s CPU, 50 calls, 25s wall clock, 200KB result. Your code runs inside an async function; use `await`; console.log is captured. // The `abs` object available in execute(): interface Abs { searchTables(input: { query?: string; geography?: string; frequency?: "A"|"S"|"Q"|"M"|"W"|"D"; limit?: number }): Promise<{ results: TableSummary[]; total: number }>; describeTable(table: string): Promise<{ table: TableSummary; dimensions: { id: string; position: number; optionCount: number; options?: { code: string; label: string|null }[] }[]; keyFormat: string }>; searchOptions(input: { table: string; dimension: string; query: string; limit?: number }): Promise<{ options: { code: string; label: string|null; parent?: string|null }[]; total: number }>; getData(input: { table: string; select?: Record<string, string|string[]>; startPeriod?: string; endPeriod?: string; lastN?: number; firstN?: number; maxRows?: number }): Promise<{ key: string; rows: { series: string; period: string; value: number|null; unit?: string|null }[]; rowsReturned: number; truncated: boolean; seriesMatched: number; fullDataUrl: string }>; } interface TableSummary { id: string; name: string|null; seriesCount: number; frequencies: string[]; coverage: { from: string|null; to: string|null }; dimensions: string[]; family: string|null; geography: string|null; matchedOptions?: { dimension: string; code: string; label: string|null }[] } // getData throws an Error whose message is JSON: { reason, dimension, message, validOptions?, alternatives? } — catch it, read validOptions, correct and retry.

  • get_dataFree

    Fetch observations. `select` maps dimension ids to option codes or labels (several allowed); omitted dimensions match everything. The selection is verified live against ABS before fetching, so a call that succeeds always returns real data. Returns up to maxRows observations (default 500, most recent 12 periods per series unless a period range is given), a summary, and the URL for the complete pull. If an option or combination does not exist you are asked to choose from the valid ones.

  • searchwrite actionFree

    Run JavaScript against the whole catalogue document — every table, its dimensions, coverage and small-dimension options — in an isolated sandbox with no network. Use it to answer questions the fixed verbs make awkward: 'which tables have both an SA2 geography and a quarterly frequency?', 'list every dimension name and how often it appears', 'find codelists whose labels mention rent'. Your code runs inside an async function with `catalogue` in scope; `return` a JSON-serialisable value; console.log output is captured. // The `catalogue` object available in search(): interface Catalogue { provenance: { runId: string; observedAt: string }; corpus: { tables: number; seriesConfirmed: number; seriesImpliedByMetadata: number; density: number }; tables: Record<string, { // keyed by table id, e.g. catalogue.tables.CPI id: string; name: string|null; description: string|null; seriesCount: number; frequencies: string[]; coverage: { from: string|null; to: string|null }; density: number|null; family: string|null; geography: string|null; topics: string[]; dimensions: { id: string; position: number; codelist: string|null; optionCount: number; literal: boolean }[]; }>; options: { literal: Record<string, Record<string, string|null>>; // codelist id -> { code: label } for small codelists (<=64 options) branded: Record<string, number>; // large codelists -> option count (use abs.searchOptions in execute) }; }

  • search_optionsFree

    Find option codes by label text within one dimension of one table — e.g. a suburb name in a geography dimension. Returns codes to use in get_data's select.

  • search_tablesFree

    Find ABS statistical tables (dataflows) by topic words, geography level or frequency. Matches table names, topics and dimension names, and also option labels inside dimensions — a search for 'rent' finds CPI through its INDEX option 'Rents' and reports the match in matchedOptions. Census tables published at several geography levels are collapsed to one result with familyGeographies listing the others (use the geography filter to pick one). Every result is confirmed to serve data — nothing here comes from documentation alone. Start here, then describe_table.

Public scan report

scanner v0.1.10 · 2026-10-15 · same rubric, same numbers if you re-run it

1 high1 low
  • –Code scanremote-only server, no package to scann/a
  • Live reliabilityremote reachable in 1864ms20/20
  • Tool poisoning6 tool descriptions checked13/15
  • Auth qualityopen endpoint exposes 2 write-action tools with no auth3/15
  • Maintenancelast push 17 days ago15/15
  • Maintainer identitynamespace and repository owner differ; GitHub account older than a year6/10

Findings (2)

  • highWrite-action tools reachable without authenticationauth.open-write
  • lowUnusually long tool description (over 2,000 characters)poison.long-description
    tool execute: …Run JavaScript in an isolated sandbox whose only capability is `abs`, a client with the same four verbs as this server (searchTables, describeTable, searchOptions, getData) and the same guarantees: every selection is verified against ABS before fetching. Use it for multi-series or multi-table analysis — fetch several series, compute growth rates, rank capitals, join tables — and `return` only the computed result, so large payloads never reach the conversation. No network beyond `abs`. Budgets: 10s CPU, 50 calls, 25s wall clock, 200KB result. Your code runs inside an async function; use `await`; console.log is captured. // The `abs` object available in execute(): interface Abs { searchTables(input: { query?: string; geography?: string; frequency?: "A"|"S"|"Q"|"M"|"W"|"D"; limit?: number }): Promise<{ results: TableSummary[]; total: number }>; describeTable(table: string): Promise<{ table: TableSummary; dimensions: { id: string; position: number; optionCount: number; options?: { code: string; label: string|null }[] }[]; keyFormat: string }>; searchOptions(input: { table: string; dimension: string; query: string; limit?: number }): Promise<{ options: { code: string; label: string|null; parent?: string|null }[]; total: number }>; getData(input: { table: string; select?: Record<string, string|string[]>; startPeriod?: string; endPeriod?: string; lastN?: number; firstN?: number; maxRows?: number }): Promise<{ key: string; rows: { series: string; period: string; value: number|null; unit?: string|null }[]; rowsReturned: number; truncated: boolean; seriesMatched: number; fullDataUrl: string }>; } interface TableSummary { id: string; name: string|null; seriesCount: number; frequencies: string[]; coverage: { from: string|null; to: string|null }; dimensions: string[]; family: string|null; geography: string|null; matchedOptions?: { dimension: string; code: string; label: string|null }[] } // getData throws an Error whose message is JSON: { reason, dimension, message, validOptions?, alternatives? } — catch it, read validOptions, correct and retry.…
Overall 76/100. Components that don't apply are left out of the denominator. Any critical finding is an F.RubricAppeal a findingJSON

How ABS Data (observed) compares

Against every graded MCP server, from our own scans and uptime checks. Updated after each scan run.

Safety score
76/100
higher than 48% of 48,243 servers
Tools
6
the typical server has 7
Tools that change things
33%
typical: 0%. Fewer write tools means less an agent can break
Uptime
100%
of our recent checks; the typical remote server answers 100%
Response time
338 ms
faster than 48% of 30,027 servers

What its tools let an agent do: 4 read · 1 change · 1 run or deploy

Install ABS Data (observed) in Claude Code, Cursor or VS Code

claude mcp add --transport http abs-data https://abs-data-front-door.aicolab.workers.dev/mcp
Add to Cursor

What the publisher says

From the ABS Data (observed) repository's README, as published. We do not edit it. Read it on GitHub

ABS Data API — cartography and front door

The Australian Bureau of Statistics Data API is valuable and badly described. This project measured what it actually serves — every dataflow, by retrieval — and exposes the result as a typed front door: an MCP server and an HTTP API that only ever offer what exists.

What was found

14,761,557,818 — a density of 4.21%, overstated 23.8×.

  • 621,536,714 series exist across 1,227 dataflows. ABS's metadata implies

nothing real is unadvertised. The entire overstatement is combinatorial.

  • The per-dimension metadata is exact: every advertised option exists, and

the keys — almost entirely hierarchical geography encoded as separate dimensions (STATE is a function of REGION in 664 flows). The remaining 19% is not reducible to a single-dimension dependency.

  • 81.0% of series sit in flows whose sparsity is principled and verified in

broken (HTTP 200, malformed JSON). Eight of fifteen documented behaviours do not conform. See reports/.

  • detail=serieskeysonly — the endpoint that should answer "what exists" — is

DESIGN.md is the full record: verified API findings, every decision and why, and the corrections made along the way.

Layout

packages/schema      Drizzle schema (declared vs observed, never joined), shared CSV/period parsing
packages/crawler     structural crawl, observed probe, conformance suite, sizing
packages/catalogue   delta report, sparsity analysis, contract generator, OpenAPI emitter, artifact
packages/contract    the corrected contract: stable Zod verbs + generated tables/options (per crawl)
packages/worker      the front door: MCP door (SDK v2) + HTTP door (oRPC + Scalar) + RPC door (Cap'n Web) on one Worker
packages/sdk         @abs/sdk — typed TypeScript client over the RPC door (connect(), invalidSelectionFromError())
reports/             delta.md/json, sparsity.md/json, openapi.json, abs-report-draft.md
data/                local SQLite catalogue (~400GB), raw archive, D1 import SQL — not committed

Running it

pnpm install
pnpm db:migrate                       # local SQLite at data/abs-catalogue.sqlite

pnpm crawl:structural                 # 7 bulk requests, ~1 minute
pnpm crawl:probe -- '--key-values=*'  # every dataflow by retrieval; ~24h, 20GB heap for the giants
pnpm report:delta && pnpm report:sparsity
pnpm generate:contract                # -> packages/contract/src/generated, data/d1/*.sql
pnpm --filter @abs/catalogue emit:openapi

Shortened. The full README is on GitHub.

Nothing above is checked by us. What we check is on the safety report.

ABS Data (observed): common questions

Is ABS Data (observed) MCP server safe?
Mostly: it is graded B (76/100). Read the ABS Data (observed) safety report
How do I install ABS Data (observed)?
It runs remotely at abs-data-front-door.aicolab.workers.dev. Add it to Claude Code, Cursor, VS Code or Claude Desktop with the snippets above, or call it through the mcp.market gateway without installing anything.
Does ABS Data (observed) need an API key?
Not as far as the registry entry and our scan can tell: no credentials are declared or required.
Is ABS Data (observed) maintained?
The last commit was 38 days ago (2026-09-28). The latest release is v0.1.0.
Is ABS Data (observed) up?
100% of our last 28 checks got an answer. We check remote servers about four times a day.
How does ABS Data (observed) compare with other MCP servers?
safety score 76/100 (higher than 48% of 48,243 servers); tools 6 (the typical server has 7); tools that change things 33% (typical: 0%. Fewer write tools means less an agent can break). Full comparison.