fetch_dataset_csv

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

With no annotations, the description carries the full burden and does well: it discloses pagination behavior and the "truncated" flag, the 5000-row cap with rows possibly being large, the aggregate-total pitfall, and that empty aggregate does not guarantee summable rows. It omits any mention of error behavior for invalid dataset_ids or permissions.

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?

Purpose is front-loaded, then detail escalates from identifier to aggregate semantics to filters to paging — a sensible order. The paging sentence ("continue at offset + returned") is slightly compressed and takes a reread, but no line is wasted.

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 4-parameter tool with no annotations and no output schema, the description covers the response shape (label-and-code formatting, aggregate and unlabeled_dimensions columns, truncation) well enough to call it correctly. What it lacks is any statement of failure modes or how to discover valid dataset_ids and dimension codes beforehand.

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 description coverage is 0%, so the description must compensate and it does: dataset_id is explained with a concrete example, filters get exact syntax and a worked example with the caveat that values are the <code> parts, and limit/offset are given defaults semantics, a max, and a continuation rule.

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?

States a specific verb and resource ("Fetch an OGD dataset's data") plus a distinguishing output trait (codes resolved to German labels, formatted "label <code>"). It never mentions the sibling fetch_dataset_json, so the agent must infer the CSV-vs-JSON distinction from the tool names alone.

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?

Operational guidance is strong — how to filter, how to page with offset/limit, and the caveat about summing aggregate rows — but there is no explicit statement of when to choose this tool over fetch_dataset_json or search_dataset. Usage is implied by the output format rather than stated as a selection rule.

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