APIbase

Statistik Austria Dataset Data

statistik-austria.series.dataset_data

Read-onlyIdempotent

Fetch paginated rows from a Statistik Austria dataset CSV by dataset ID, with configurable limit and offset, to retrieve statistical data for analysis.

Instructions

Fetch rows from a Statistik Austria dataset's CSV data file (dataset_id from statistik-austria.dataset_search). Column codes are opaque (e.g. "C-STAATS-0", "F-VESTE_AM") — use statistik-austria.dataset_metadata for column meanings and statistik-austria.category_codes to decode category values (e.g. "STAATS-9"). Rows capped 1-200 (default 20). Data: data.statistik.gv.at (Statistik Austria), no auth required.

Input Schema

Name	Required	Description
`limit`	No	Max rows to return (1-200, default 20).
`offset`	No	Number of rows to skip, for paging through a large dataset.
`dataset_id`	Yes	Statistik Austria dataset id, from statistik-austria.dataset_search (e.g. "OGD_konjunkturmonitor_KonMon_1").

Output 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.

Addedv2.2.0Oct 29, 2026

TDQS

A4.5/5.0

Behavior4/5

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

Annotations already cover read-only/idempotent/non-destructive safety, and the description adds meaningful context: opaque column codes requiring decoding, the row cap, the data source URL, and no-auth requirement. It does not describe pagination/offset behavior beyond what annotations imply, but it adds useful operational details without contradicting annotations.

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

Conciseness5/5

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

Every sentence earns its place: core action, source identifier, decoding instructions, limits, and data provenance. The critical information is front-loaded, and the description is compact and easy to scan.

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

Completeness5/5

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

Given the rich input schema, output schema, and annotations, the description leaves no essential gap. It covers the data source, authentication, related-tool routing, column/value decoding, and row limits. An agent can correctly select and invoke this tool without additional documentation.

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

Parameters3/5

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

Input schema covers 100% of parameters, so the description is not required to explain limit/offset/dataset_id, and the schema already includes the example dataset_id and bounds. The description adds minor context (e.g., dataset_id origin, row cap default), but does not substantially enrich parameter understanding beyond the schema.

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 ('Fetch rows'), a resource ('a Statistik Austria dataset's CSV data file'), and a source for the required identifier, clearly distinguishing this tool from statistik-austria.dataset_search and metadata/category tools. It states exactly what the tool does without ambiguity.

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 tells the agent to get dataset_id from statistik-austria.dataset_search, and directs to dataset_metadata for column meanings and category_codes for value decoding. This provides clear routing among sibling tools and an explicit when-to-use/alternative guidance pattern.

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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