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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint as safe. The description adds valuable context: one row per registered vehicle, fleet counts, breakdown dimensions, and that average/median range and CAFV eligibility are reported. No contradiction with annotations.

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 two paragraphs with front-loaded purpose. Every sentence adds value. It is concise but could be slightly more succinct by merging the example questions into the first paragraph. Still, it is well-structured without waste.

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 has 10 parameters and no output schema. The description covers return values (counts, breakdowns, average range, CAFV eligibility) and provides specific query examples. It lacks information on sorting or null handling but is otherwise comprehensive for a read-only count tool.

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 coverage is 100%, so baseline is 3. However, the description adds meaningful details beyond the schema: it explains that make/model are upper case and matched as substring, gives the limit default and max, and lists group_by options with examples. This helps the agent use parameters correctly.

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 explicitly states it counts electric vehicles registered in Washington State, with breakdowns by geography, make, model, etc. It distinguishes itself from the sibling tool 'wa_dmv_registration_transactions' by clarifying it is fleet counts, not sales. The verb 'count' and resource 'EV population' are specific.

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 provides clear example questions (e.g., 'how many Teslas are in King County') that signal appropriate use cases. It does not explicitly state when not to use it, but the context is sufficient given the tool's clear scope. It distinguishes from the similar sibling by noting it's fleet counts, not transaction-based.

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