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

Annotations already establish read-only, idempotent, non-destructive behavior. The description adds valuable context by naming the model version, specifying the returned metrics and units, and framing the operation as an estimate, which clarifies the computational nature beyond the annotation hints.

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 three tight sentences: purpose/model, outputs with units, and audience/use case. Every sentence contributes distinct value, and the core action is front-loaded. There is no filler or redundancy.

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 9-parameter tool with no output schema, the description covers the essential selection and invocation context: what it does, what it returns, and who it serves. Parameter mechanics are fully documented in the schema, so the description need not repeat them. A minor gap is the absence of any note about location-input requirements, though the schema handles that.

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 baseline is 3 and the description does not need to restate parameter meanings. The description does reinforce the relevance of system_capacity through 'size and estimate solar arrays', but it adds no parameter-specific detail beyond what the schema provides.

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 a specific operation: estimating solar PV production using NREL's PVWatts v8 model, and enumerates the outputs (annual/monthly AC energy, solar resource, capacity factor). This distinguishes it from sibling tools like nrel_solar_resource and nrel_utility_rates by naming the model and the full production-estimation scope.

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 identifies target users and a broad use case ('size and estimate solar arrays'), which implies when the tool is appropriate. However, it does not explicitly cite alternative tools or conditions for choosing this tool over siblings such as nrel_solar_resource, leaving some selection ambiguity.

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