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

Annotations already declare readOnlyHint=true, idempotentHint=true, destructiveHint=false, so the safety profile is covered. The description adds valuable behavioral context by specifying what freshness_signal_source values mean (last_modified, content_parse, or none) and that the tool can distinguish unknown-freshness from stale data, which is not evident from annotations alone.

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 only two sentences. The first sentence states purpose and return fields, the second gives usage context and adds nuance about freshness signals. Every word earns its place, with zero fluff and strong front-loading.

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?

The tool is simple (one parameter, no nested objects) and has both input and output schemas. The description covers the key differentiator (freshness signal semantics), provides usage context via search_datasets, and the output schema can explain return values. There are no obvious gaps for an AI agent to use this tool 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%: dataset_id has a clear description with an example. The description does not add parameter semantics beyond 'one dataset id', which is already clear from the schema. Per calibration, baseline 3 applies when schema does the heavy lifting, and no extra parameter detail is needed.

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 states the tool 'Return full detail for one dataset id' and lists specific fields (health status, last-verified timestamp, content_freshness_date, freshness_signal_source). This distinguishes it from siblings like search_datasets (finding datasets) and find_stale (finding stale datasets), and even from get_provenance by emphasizing health/freshness in addition to provenance metadata.

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 explicitly says 'Use to fetch the provenance/citation metadata for a dataset found via search_datasets' and 'distinguish unknown-freshness from proven stale data', giving clear context on when this tool is appropriate. It does not mention explicit exclusions or 'instead' alternatives, but the context is sufficient for an AI agent to select it over search_datasets.

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