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

Annotations already cover the read-only and open-world profile, but the description adds real behavioral detail: results are capped at 8 versions and returned newest first. It does not cover failure modes (e.g., no match found), so it falls slightly short of full disclosure.

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 short sentences: purpose first, rationale second, behavior and fallback third. The middle sentence on plug/power/battery rationale is arguably expendable, but it is front-loaded and readable with no wasted filler.

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 single-parameter lookup with no output schema, the description supplies everything needed: the return cardinality, sort order, and an ambiguity-resolution procedure. Nothing required to invoke it correctly is missing.

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 single 'query' parameter is already documented with an example. The description reinforces query refinement (adding a year for precision) but adds no syntax or format detail beyond the schema, so the baseline 3 applies.

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 and resource ('Find the exact version of the driver's electric car') and names the concrete output goal ('to get its vehicle_id'). It also explains why the vehicle matters (it determines compatible plugs, power draw, and battery size, which drive price), which no sibling tool addresses.

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?

Gives an explicit fallback path when results are ambiguous: if versions differ in battery or power, ask the driver which one, or request the year and re-search with a more precise query. This tells the agent both when to use the tool and how to disambiguate afterward.

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