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

Beyond readOnly/idempotent annotations, the description discloses meaningful runtime behavior: partial degradation never fails the whole call, responses are Ed25519-signed, datasets are live, and the first 3 calls are free. This enriches the safety and invocation model beyond what annotations alone convey.

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 front-loads the core value proposition and packs no filler; every clause adds information. The minor 'FRE' typo and dense comma-list formatting keep it from a perfect score.

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?

Given the optional 'input' parameter is ambiguous and the description does not clarify how to pass product parameters or select datasets, an agent cannot fully infer request construction. However, the rich annotation metadata, output schema, and dataset list provide a solid base, so the gap is moderate.

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 100%, so the baseline is 3; the tool description does not add parameter meaning. The only parameter's schema description ('parametros del producto (JSON o texto)') is generic, and the description does not explain what product parameters are valid or how to request subsets of the 7 datasets.

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 clearly identifies a macro intel bundle and enumerates its 7 dataset types (commodity macro, market sentiment, stablecoin supply, etc.), making its purpose concrete. It stops short of an explicit action verb and does not name sibling alternatives, but 'in ONE call' and the dataset list differentiate it as the bundle/aggregation tool.

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?

'Cheaper than buying each separately' explicitly signals to prefer this over separate per-dataset purchases, and '7 datasets in ONE call' states the core batching use case. It does not provide explicit when-not-to-use guidance, such as using a single sibling when only one dataset is needed.

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