ABS Data (observed)

ABS Data API — cartography and front door

This project measured what the Australian Bureau of Statistics Data API 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.

Front door	https://abs-data-front-door.aicolab.workers.dev
Add to claude.ai / any MCP client as a connector	https://abs-data-front-door.aicolab.workers.dev/mcp (registry name io.github.AI-CoLab/abs-data)
MCP (2026-07-28, stateless) — 4 fixed verbs + Code Mode search/execute, resources, prompts	https://abs-data-front-door.aicolab.workers.dev/mcp
RPC door (Cap'n Web; what @abs/sdk speaks — HTTP batch or WebSocket)	https://abs-data-front-door.aicolab.workers.dev/rpc
HTTP API	https://abs-data-front-door.aicolab.workers.dev/api/tables
Scalar reference	https://abs-data-front-door.aicolab.workers.dev/api/docs
Corrected OpenAPI	https://abs-data-front-door.aicolab.workers.dev/api/openapi.json
The menu as one document	https://abs-data-front-door.aicolab.workers.dev/api/catalogue.json
Live browsable catalogue	https://abs-data-front-door.aicolab.workers.dev/catalogue
Static snapshot (claude.ai artifact)	https://claude.ai/code/artifact/d0635dee-5cce-40dd-bfa2-7ab7dd03e5bd

What was found

• 621,536,714 series exist across 1,227 dataflows. ABS's metadata implies 14,761,557,818 — a density of 4.21%, overstated 23.8×.
• The per-dimension metadata is exact: every advertised option exists, and nothing real is unadvertised. The entire overstatement is combinatorial.
• 81.0% of series sit in flows whose sparsity is principled and verified in 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.
• detail=serieskeysonly — the endpoint that should answer "what exists" — is broken (HTTP 200, malformed JSON). Eight of fifteen documented behaviours do not conform. See reports/.

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

cd packages/worker
npx wrangler d1 execute abs-catalogue --local --file=../../data/d1/00-schema.sql   # then each data file
npx wrangler dev
ABS_MCP_URL=http://localhost:8787/mcp npx tsx test/protocol.ts                     # ~50 checks, all doors
pnpm --filter @abs/sdk example                                                     # the SDK against production
ABS_URL=http://localhost:8787 npx tsx test/limits.ts                               # rate limits (spends a minute's budget)

Rate limits, per client address: 600 requests/min overall; 120/min for data pulls (each is a live ABS call); 20/min for Code Mode execute. Over the limit, HTTP doors answer 429 with Retry-After; an MCP tool call gets a tool error.

Using the SDK from TypeScript:

import { connect, invalidSelectionFromError } from "@abs/sdk";

const abs = connect(); // https://abs-data-front-door.aicolab.workers.dev/rpc
const [hits, cpi] = await Promise.all([          // one HTTP batch
  abs.searchTables({ query: "cpi rent", limit: 3 }),
  abs.describeTable("CPI"),
]);
try {
  const data = await abs.getData({ table: "CPI", select: { REGION: "Sydney", INDEX: "10001" }, lastN: 4 });
} catch (err) {
  const invalid = invalidSelectionFromError(err); // typed: reason, dimension, validOptions from observation
}

The probe is resumable: rerunning skips flows already recorded as complete. For the largest census flows run with NODE_OPTIONS=--max-old-space-size=20480.

Gotcha: wrangler keys the local D1 state by database_id. Changing the id in wrangler.toml (e.g. after d1 create) gives wrangler dev a fresh, empty local database — re-run the local import.

Principles

• Observed-only. Declared metadata steers the crawler and feeds the defect report; it never enters what a caller sees. Availability is empirical: observed options from the catalogue, exact combination checks live against ABS's availableconstraint (which agrees with the probe on every dimension of every flow).
• One contract, derived doors. Every crawl regenerates one contract; the MCP verbs, the HTTP routes, the OpenAPI document and the docs all read or render it. Nothing hardcodes the surface, so nothing drifts.
• Only calls that return data. A selection is verified before it is forwarded; an invalid one comes back with the observed valid options — as an MRTR input_required on MCP, a typed 422 on HTTP.