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

Annotations already declare read-only, idempotent, non-destructive, open-world behavior, and the description adds genuinely new operational facts: a 20-row sample cap, a 5 MB read limit, and that personal-data-looking columns (names, IDs, emails, phones) are withheld. That privacy-filtering disclosure is exactly the kind of trait annotations cannot express.

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 paragraphs, front-loaded with what is returned before the constraints. Each sentence carries a distinct fact (return shape, file scope and size cap, PII withholding, fidelity of values) with 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?

With an output schema present, the description need not explain return structure, yet it still summarizes the payload (columns, sample rows, licence). Combined with annotations covering the safety profile and complete schema coverage, an agent has everything needed to call it correctly.

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 100%, and both parameters are already documented there (dataset_id from find_datasets, file_index selection with a stated default). The prose adds no parameter-level syntax or format detail beyond what the schema provides, so the baseline 3 applies.

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 (read) plus resource (datos.gob.es dataset) and enumerates exactly what is returned: columns, up to 20 sample rows, and the licence. This clearly separates it from the search-oriented sibling find_datasets.

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 establishes the operating context (only CSV and JSON files listed in the catalogue, only the first 5 MB) and the schema ties dataset_id back to find_datasets, so an agent can infer the follow-up-after-search workflow. It stops short of an explicit when-not-to-use statement or naming an alternative tool.

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