estimate_water

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

No annotations are provided, so the description carries the behavioral transparency burden. It meaningfully discloses defaults (500 output tokens), the role of reasoning_effort when usage is unknown, provider/region overrides, and the return shape (ranges, summary, assumptions). This goes well beyond the schema, though it does not mention failure modes or how unknown models are handled.

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 front-loaded with a clear purpose sentence and then uses compact, information-dense sentences for parameter usage and return values. No sentence is wasted; every clause contributes to correct invocation or interpretation.

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 10-parameter tool with no annotations and no schema descriptions, the description is quite complete: it covers the main estimation path, key overrides, defaults, and output expectations. The main gap is that the required model parameter is not elaborated, and there is no pointer to sibling list_known_models for discovering valid model identifiers.

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 0%, and the description compensates by explaining prompt, reasoning_effort, provider, region, include_embodied, and token-count behavior. However, it refers to token counts collectively rather than explicitly mapping input_tokens, cached_tokens, output_tokens, and reasoning_tokens, leaving a small but meaningful gap.

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 opens with a specific verb and resource: 'Estimate the freshwater consumed (mL) by one LLM call.' It clearly states the tool's scope and the main input choices (token counts vs. prompt). This differentiates it from siblings like compare_models and pick_low_water_model, which focus on comparison/recommendation rather than single-call estimation.

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 gives clear context on when to provide token counts versus prompt text, how reasoning_effort models thinking tokens, how provider/region override the default profile, and when to set include_embodied=false. It does not explicitly name sibling alternatives or exclusion criteria, so it stops short of a 5.

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