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8 clients · secrets stay placeholders

JSON (mcpServers)Claude CodeCodexOpenCodeCursorVS CodeGemini CLIGoose

{
  "mcpServers": {
    "abs-data": {
      "type": "http",
      "url": "https://abs-data-front-door.aicolab.workers.dev/mcp"
    }
  }
}

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

[mcp_servers.abs-data]
url = "https://abs-data-front-door.aicolab.workers.dev/mcp"

{
  "$schema": "https://opencode.ai/config.json",
  "mcp": {
    "abs-data": {
      "type": "remote",
      "url": "https://abs-data-front-door.aicolab.workers.dev/mcp",
      "enabled": true
    }
  }
}

{
  "mcpServers": {
    "abs-data": {
      "type": "http",
      "url": "https://abs-data-front-door.aicolab.workers.dev/mcp"
    }
  },
  "deeplink": "cursor://anysphere.cursor-deeplink/mcp/install?name=abs-data&config=eyJ0eXBlIjoiaHR0cCIsInVybCI6Imh0dHBzOi8vYWJzLWRhdGEtZnJvbnQtZG9vci5haWNvbGFiLndvcmtlcnMuZGV2L21jcCJ9"
}

{
  "servers": {
    "abs-data": {
      "type": "http",
      "url": "https://abs-data-front-door.aicolab.workers.dev/mcp"
    }
  }
}

{
  "mcpServers": {
    "abs-data": {
      "httpUrl": "https://abs-data-front-door.aicolab.workers.dev/mcp"
    }
  }
}

extensions:
  "abs-data":
    enabled: true
    name: "abs-data"
    type: streamable_http
    uri: "https://abs-data-front-door.aicolab.workers.dev/mcp"
    timeout: 300

Quality 85 / 100good

protocol100 · weight 25

pass
protocol.modernsupports 2026-07-28 (server/discover)
pass
protocol.statelessanswers without a session
pass
protocol.transportstreamable HTTP
pass
protocol.list_ttltool list is cacheable (ttlMs 86400000)

authorizationn/a · weight 15

n/a
auth.prm, auth.as_metadata, auth.cimdno remote uses OAuth
n/a
auth.secret_in_urlno templated URL

tool hygiene64 · weight 30

pass
tools.descriptionsevery tool has a description
warn
tools.description_length2 tools have descriptions over 1,024 characters, which crowds the context
pass
tools.schemasevery tool has an object input schema
fail
tools.annotationsno tool declares readOnlyHint or destructiveHint, so clients cannot tell safe calls from risky ones
fail
tools.directory_hintsno tool declares all four of readOnlyHint, destructiveHint, idempotentHint and openWorldHint; missing: search (readOnlyHint, destructiveHint, idempotentHint, openWorldHint); describe_table (readOnlyHint, destructiveHint, idempotentHint, openWorldHint); search_options (readOnlyHint, destructiveHint, idempotentHint, openWorldHint); …
pass
tools.token_costabout 1,880 tokens to load every tool
pass
tools.api_dumptools are not a one-to-one API dump

stability100 · weight 15

pass
stability.changesno tool changes in 30 days
pass
stability.rug_pullno tool changed its meaning under the same name

dependenciesn/a · weight 15

n/a
deps.known_vulns, deps.mcp_sdk_version, deps.resolvableno npm or PyPI package

6 tools, about 1,880 tokens to load them all · tool set 617294f4f55d · checked 2026-10-19 13:37 UTC

Tool changes

No tool definition changes recorded. A server's first observation is its baseline; later probes record what changes.

Checks run on what our credential-free, read-only probes observe; tools are never called and no code audit is performed. How it is computed.

Trust 89 / 100

provenance75

liveness98

freshness85

hygiene100

+repository +website +reachable +uptime-95% ~updated-40d-ago

Derived from observable signals (official registry feed and our own credential-free probes); no code audit performed. How it is computed.

Endpoints

remote	auth	reachable	uptime 30d	p50	protocol	last ok
`https://abs-data-front-door.aicolab.workers.dev/mcp` streamable-http	none	yes	95%	448 ms	2026-07-28	2026-10-19

Tools (6)

`describe_table`	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.
`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.
`get_data`	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.
`search`	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_options`	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_tables`	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.

Schemas: list_tools.

Similar MCP servers

Official server.json

{
  "name": "io.github.SamJB123/abs-data",
  "title": "ABS Data (observed)",
  "$schema": "https://static.modelcontextprotocol.io/schemas/2025-12-11/server.schema.json",
  "remotes": [
    {
      "url": "https://abs-data-front-door.aicolab.workers.dev/mcp",
      "type": "streamable-http"
    }
  ],
  "version": "0.1.0",
  "repository": {
    "url": "https://github.com/AI-CoLab/abs-data-mcp",
    "source": "github"
  },
  "websiteUrl": "https://abs-data-front-door.aicolab.workers.dev",
  "description": "Australian Bureau of Statistics data: 1,227 tables, offering only options confirmed to serve data"
}