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

Annotations already declare readOnly, idempotent, non-destructive and open-world, so the safety profile is covered. The description adds real context beyond that: it is a static enumeration whose entries carry domain ranges, resolution, time range and field lists, and it flags that ranges are in the dataset's native simulation units — a detail an agent would otherwise get wrong.

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 sentences, front-loaded with the purpose before the entry-structure detail. The second sentence is a long enumeration but each item corresponds to a field an agent will actually consume, so little is wasted.

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 no parameters and no output schema, the description carries the burden of explaining the return shape, and it does so well by listing exactly what each entry contains. Minor gaps remain: no statement about catalog size, pagination, or whether the listing is fixed versus refreshed.

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 tool takes zero parameters, so there is nothing for the description to disambiguate and the baseline applies. It correctly avoids inventing filter arguments the schema does not have.

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 resource (the catalog of JHTDB datasets) and its scope (those available for point queries), then enumerates the physics cases covered. It also implicitly distinguishes itself from jhtdb_point_query, the execution sibling, by positioning itself as the lookup layer.

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 phrase "the dataset code to pass to jhtdb_point_query" gives a clear routing instruction: use this first to discover a code, then query. It lacks explicit when-not guidance (e.g., call this only once / cache the result) and any prerequisites such as auth for the query tool.

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