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by io.github.whiteknightonhorse
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Statistik Austria Dataset Metadata

statistik-austria.reference.dataset_metadata
Read-onlyIdempotent

Get full metadata for one Statistik Austria dataset (dataset_id from statistik-austria.dataset_search) — title, notes, tags, license, update frequency, resource list, column meanings (attribute_description), and the category_dimensions list needed for statistik-austria.category_codes. Data: data.statistik.gv.at (Statistik Austria), no auth required.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "dataset_id": "<dataset_id>"
    },
    "name": "statistik-austria.reference.dataset_metadata"
  }
}

Input Schema

NameRequiredDescriptionDefault
dataset_idYesStatistik Austria dataset id, from statistik-austria.dataset_search (e.g. "OGD_veste309_Veste309_1" for the 2018 earnings-structure survey, "OGD_konjunkturmonitor_KonMon_1" for the economic-monitor series).

Output Schema

NameRequiredDescriptionDefault
errorNoPresent only when the call failed. Includes error code, message, request_id, and any provider-specific extras.
resultNoTool 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.

  1. First observed

TDQS

A4.3/5.0
Behavior4/5

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

Annotations already establish this as a safe, read-only, idempotent operation, so the description does not need to repeat that. It adds useful context beyond the annotations: no authentication is required, the data source is data.statistik.gv.at, and the output includes a specific prerequisite list for another tool. No contradiction exists between the description and 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?

The description is two sentences, front-loads the core action, and uses the rest to list valuable return fields and dependencies. Every sentence earns its place; the field enumeration is informative rather than filler.

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?

For a single-parameter metadata lookup with a rich output schema, the description is complete: it identifies the input source, the output contents, the downstream use of category_dimensions, and the auth/source details. An agent has everything needed to call the tool correctly.

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?

Schema description coverage is 100%, with the dataset_id parameter already documented and given concrete examples. The description reinforces that dataset_id comes from dataset_search but does not add substantial meaning beyond what the schema provides. Baseline 3 is appropriate because the schema carries the parameter documentation burden.

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 opens with a specific verb and resource: 'Get full metadata for one Statistik Austria dataset.' It enumerates the returned contents (title, notes, tags, license, update frequency, resource list, attribute_description, category_dimensions), making the tool's scope unmistakable and clearly distinguishing it from dataset_search and series.dataset_data.

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

Usage Guidelines4/5

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

The description tells the agent that dataset_id comes from statistik-austria.dataset_search, establishing the correct preceding step. It also notes that the returned category_dimensions list is needed for statistik-austria.category_codes, which routes the agent to the next tool. It does not explicitly state when not to use this tool (e.g., when raw series data is needed), but the context is clear.

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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