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

Annotations already declare readOnlyHint, idempotentHint, openWorldHint, and destructiveHint=false. The description adds valuable non-schema context: return values are in the dataset's native simulation units, with a concrete distinction between dimensionless DNS code units and physical meters/seconds for LES datasets. It also discloses the use of a public testing token with a capped small-query allowance, which is important operational context. It does not detail the exact return shape (e.g., three-component vector vs. scalar vs. gradient tensor), keeping it from a 5.

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 tightly written sentences with zero filler. The core purpose comes first, followed by the essential units and authorization context.

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 query tool with no output schema and rich annotations, the description covers the crucial missing pieces: native return units and the prerequisite to check jhtdb_datasets. It remains slightly incomplete in not describing the exact return structure (vector vs. scalar vs. tensor) or failure behavior for out-of-domain points, but the essential information is present.

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%, so the schema already documents every parameter, including units, enums, defaults, and point limits. The description reinforces the up-to-10-points and single-time constraints and the native-units caveat, but adds little semantic detail beyond what the schema provides. Baseline 3 is 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 first sentence states a specific verb (interpolated values), the resources (velocity, pressure, velocity-gradient), the quantitative limits (up to 10 points, single time), and the domain (JHTDB reference DNS/LES dataset). This distinguishes it cleanly from the sibling metadata tool jhtdb_datasets.

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?

It explicitly directs the agent to call jhtdb_datasets first to learn the dataset's domain and time range, which is the key prerequisite for valid points/time. There are no exclusions or named alternatives for different query types, but none of the siblings appear to be competing point-query tools, so the guidance is adequate.

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