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

With readOnlyHint=true already covering the safety profile, the description adds value by describing the read action as 'Free discovery' and enumerating the behavioral outputs: coverage, freshness, tool guidance, and example queries. It does not contradict the annotation.

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?

Three sentences deliver the tool's purpose, return contents, and usage sequence with no filler. The most important information is front-loaded ('Free discovery') and every sentence earns its place.

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?

For a simple one-parameter, read-only tool with no output schema, the description sufficiently enumerates what the agent will receive and when to call it. Combined with the schema, nothing essential is missing for correct invocation.

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 single parameter pack_id is fully documented in the schema, including its source from get_catalog, so the schema carries the semantic burden. The description adds no parameter-specific meaning, making the baseline 3 appropriate.

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 a specific verb and object—'Returns detailed metadata, coverage, freshness, preferred canonical tool guidance, and first-query examples for one pack'—which makes the resource and scope unmistakable. It also positions the tool as a discovery/pre-query step rather than a data-returning tool, distinguishing it from query_dataset.

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 contextual timing: 'Call this before querying a new pack so you can see time shape, coverage limits, and the paste-ready first query.' It does not explicitly name sibling tools or state when not to use it, but the intended workflow is evident.

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