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

Annotations already mark the tool as read-only, idempotent, and non-destructive. The description adds valuable context: data covers '7,758 awards from 2015 onward totalling about $17.2M' and includes grouping and filtering options. No contradictions 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 concise, covering key functionality in a few sentences without redundancy. It front-loads the main action ('Look up Delaware Clean Vehicle Rebate Program awards') and efficiently lists data fields and example queries.

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 the tool's moderate complexity (6 parameters, no output schema), the description compensates by listing all returned fields (award amount, county, city, etc.) and explaining grouping. It fully informs the agent about what to expect and how to use the data.

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% description coverage for all 6 parameters, each with clear explanations. The description further enhances understanding by providing an example and explaining the 'group_by' parameter with all possible values. Together, they offer strong guidance without ambiguity.

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 clearly states the tool's purpose: 'Look up Delaware Clean Vehicle Rebate Program awards' with specific details on rebate amounts, geography, and vehicle attributes. It provides example questions to further clarify use cases, and the resource is distinct from sibling tools like de_dmv_charger_rebates or de_dmv_crash_stats.

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 implicitly guides usage by listing queries it answers (e.g., 'does Delaware have an EV rebate and how much is it') and mentioning grouping dimensions. However, it does not explicitly distinguish this tool from siblings or state when not to use it, leaving room for improvement.

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