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

Annotations already show readOnlyHint, openWorldHint, idempotentHint, and destructiveHint as false. The description adds value by specifying the number of records (776), the time range (2015 onward), and the reporting of average rebate vs. average cost. This provides meaningful behavioral context beyond the 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 a single paragraph of moderate length. It front-loads the key verb and resource, then lists covered aspects. It could be more structured (e.g., bullet points) but is still effective and concise, with no filler sentences.

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?

Given lack of output schema, the description compensates by listing the fields returned (rebate amount, cost, brand, city, ZIP, county) and the data scope (776 awards, 2015 onward). It covers the main query intent and data granularity, but could mention that results are limited by the 'limit' parameter (though the schema covers it).

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 input schema has 100% coverage with descriptions for all 5 parameters. The description does not add additional semantics beyond the schema; it only mentions the default group_by (brand) implicitly via example. With full schema coverage, the baseline is 3, and the description does not improve parameter understanding.

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 precisely states the verb 'Look up' and the resource 'Delaware EV charging-equipment rebate awards'. It includes specific example questions and distinguishes from sibling tools like de_dmv_ev_rebates (vehicle rebates) by focusing on charger rebates. The scope (776 awards since 2015) and counties are clearly mentioned.

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 lists the types of questions it answers and implies use for Delaware EV charger rebate queries. However, it does not explicitly state when to use this tool versus alternatives like de_dmv_ev_rebates or de_dmv_crash_stats, nor does it provide exclusions or prerequisites.

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