Retrieve dimension and measure metadata for an OGD dataset by dataset ID, enabling you to inspect table structure before querying coded Statistik Austria data.
Return the dimension and measure metadata for an OGD dataset. dataset_id is the Statistik Austria dataset identifier (e.g. "OGD_vpi86_VPI_1986_1").
| Name | Required | Description | Default |
|---|---|---|---|
| dataset_id | Yes |
Changes observed during successful MCP inspections.
v1.0.0Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations exist, so the description carries the full burden. It does usefully disclose that the response is metadata (dimensions and measures), not the underlying observations, which prevents a caller from expecting data rows. However, it says nothing about failure behavior for invalid dataset_ids, response size, or any access constraints.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two short sentences, both load-bearing, with the return content stated first and the parameter clarification second. No filler.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a single-parameter read tool with no output schema, the description covers what is returned only at a high level ('dimension and measure metadata') and does not indicate the shape of that metadata. Adequate to call correctly, but thin on what to expect back.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 0% — the schema only says 'Dataset Id' with no description — so the description must compensate. It does: it identifies the identifier as a Statistik Austria dataset id and gives a concrete format example ('OGD_vpi86_VPI_1986_1'), which is exactly what the schema lacks.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description states a specific verb and resource: returns 'dimension and measure metadata for an OGD dataset.' That is materially more precise than the name alone, which only says 'json.' It doesn't explicitly contrast with fetch_dataset_csv or search_dataset, so differentiation from siblings is left to the name.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Usage is implied rather than stated: an agent can infer this is the JSON-format metadata variant, but the description never says when to prefer it over fetch_dataset_csv or search_dataset, and gives no prerequisites or exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.