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

Beyond the annotations (readOnly, idempotent, openWorld), the description discloses result contents (`id`, English name, description, publication date, periodicity, author, category), the size of the catalog, and the query-filtering behavior. This provides meaningful context about what the agent will receive and how to navigate the dataset space.

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?

The description is three focused sentences, front-loaded with the core purpose, and includes practical examples. Every sentence adds useful information without fluff or repetition of schema details.

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?

Even without an output schema, the description explains what each result contains, how the `id` connects to other tools, and how to filter results. The examples and catalog-size warning give the agent enough context to decide when and how to invoke the tool.

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?

The input schema already fully documents both parameters with clear descriptions, so baseline is 3. The description adds value by giving usage advice ('pass `query` to filter unless you really want the whole catalog'), concrete examples, and explaining the meaning of the returned `id` in relation to other tools.

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 uses specific verbs ('Browse or keyword-search') and names the resource (STATEC datasets called dataflows). It distinguishes the tool from siblings by explicitly stating that the returned `id` is the dataflowRef to pass to `get_data` / `dataflow_structure`.

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?

The description gives clear guidance on when to use the tool, advises filtering with `query` to avoid the whole catalog, and provides concrete examples. It also references sibling tools (`get_data`, `dataflow_structure`) and explains how the output relates to them, enabling correct tool chaining.

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