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

Annotations declare readOnlyHint=true, so safety is covered. The description adds useful behavioral context: checkout_url enables one-click purchase and human_pitch aids operator messaging, which is not obvious from the name alone. However, it doesn't state permissions needed, whether the dataset must exist, or error behavior.

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 concise sentences. The first enumerates the returned metadata fields, front-loading the primary value. The second adds distinguishing attributes (checkout_url, human_pitch). No waste.

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?

No output schema exists, so the description carries the burden of explaining returns and does so thoroughly (schema, counts, score, pricing, pitch, URL). An agent has enough to call correctly. Slight gap remains on when-not-to-use vs siblings, but the core is complete.

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% with only one parameter (dataset_id) fully described. The description adds no syntax or format hints beyond what the schema provides. Baseline 3 is appropriate when the schema does the heavy lifting.

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 (Get) and resource (dataset metadata) and enumerates exactly what the response contains: schema, column counts, quality score, price anchoring, checkout_url, human_pitch. An agent knows precisely what this returns, distinguishing it from preview_sample (data rows) and search_datasets (discovery).

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 implies usage by mentioning a dataset_id, but doesn't state when to use this tool vs preview_sample or search_datasets. No exclusions or alternative selection guidance is provided. A reasonable agent can infer it's a detail-fetch, but the description doesn't make it explicit.

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