apibase

Statistics Denmark Table Data

statistics-denmark.data.query

Read-onlyIdempotent

Fetch statistical data from a specific Statistics Denmark table using dimension filters. Returns a JSON-stat dataset with dimension labels, a flat value array, and metadata (updated timestamp, source). Use statistics-denmark.data.tables to find the table ID, statistics-denmark.data.table_info to get valid dimension codes and values, then pass them here as a variables array — one entry per dimension you want to filter or break out by (e.g. region, sex, age, time). Any dimension left out is auto-aggregated to its total by StatBank. Use values:["*"] to include all values for a dimension (e.g. the full time series). Covers population, labour market, economy, social conditions, education, business, transport, culture, and environment statistics for Denmark.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`table_id`	Yes	StatBank table ID to query — from statistics-denmark.tables or statistics-denmark.table_info. Example: "FOLK1A" for quarterly population.
`variables`	Yes	Array of dimension filters. Any dimension left out is auto-eliminated (aggregated to its default/total value) by StatBank — include a dimension only when you need to filter or break out by it. Example: [{code:"OMRÅDE",values:["000"]},{code:"KØN",values:["TOT"]},{code:"ALDER",values:["IALT"]},{code:"CIVILSTAND",values:["TOT"]},{code:"Tid",values:["*"]}]. Response is a JSON-stat dataset with dimension labels and a flat value array.

Output Schema

TableJSON Schema

Name	Required	Description	Default
`error`	No	Present only when the call failed. Includes error code, message, request_id, and any provider-specific extras.
`result`	No	Tool response payload. Shape varies per tool — consult the tool description and inputSchema. May be an object, array, string, or number depending on the upstream provider response.

Schema Changelog

Changes observed during successful MCP inspections.

First observedSep 22, 2026

TDQS

A4.5/5.0

Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already carry the safety profile (readOnlyHint, openWorldHint, idempotentHint, destructiveHint), so the bar is lower. The description adds genuinely useful behavior beyond the annotations: the JSON-stat return format with dimension labels/flat value array/metadata, the auto-aggregation of omitted dimensions, and wildcard semantics for full time series. No contradiction with annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is long but dense and front-loaded with the core purpose. Each sentence earns its place: return format, prerequisite workflow, wildcard usage, aggregation semantics, and topical coverage. Minor redundancy with the schema's variables description (e.g., the example filter array) prevents a 5.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given an output schema exists, rich annotations cover safety, and schema coverage is 100%, the description is complete: it explains the workflow, wildcard behavior, aggregation semantics, and data scope. Missing items like auth and rate limits are not critical for a read-only, idempotent query tool with openWorldHint, so no significant gap.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100% with detailed parameter descriptions, so the baseline is 3. The description adds meaningful guidance on top: the workflow for sourcing valid table_ids and dimension codes from sibling tools, and the semantic of one entry per dimension to filter or break out by. Some duplication with the schema (auto-aggregation and '*' wildcard appear in both), but the workflow linkage is genuinely additive.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description states a specific verb+resource ('Fetch statistical data from a specific Statistics Denmark table using dimension filters') and clearly distinguishes the tool from its siblings: statistics-denmark.data.tables finds tables, statistics-denmark.data.table_info returns dimension codes, and this tool queries the data. An agent can tell them apart without opening schemas.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explicitly names the prerequisite tools and the order of use ('Use statistics-denmark.data.tables to find the table ID, statistics-denmark.data.table_info to get valid dimension codes and values, then pass them here'), and explains when to include or omit a dimension ('Any dimension left out is auto-aggregated to its total'), which is effective when-versus-alternative guidance.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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