pick_low_water_model

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

No annotations provided, so the description carries the burden. It states the tool returns the lowest-water model meeting min_tier and the savings vs. the thirstiest candidate Pdf. It does not disclose behavior when no candidate meets min_tier, nor the meaning of null min_tier. It doesn't mention any side effects (likely none), but could be more explicit about input/output token parameters' role in water estimation. The description is reasonably transparent but has gaps.

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 sentences, front-loaded with the core purpose, then usage guidance. Every sentence adds value with no 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?

With an output schema present, the description need not explain return values, but it does mention what it returns. It covers purpose, usage context, and key parameter guidance. It lacks edge-case behavior (e.g., no candidate meets the floor) but overall is fairly complete for a selection tool with a simple contract.

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 0%, so the description must explain parameters. It explains min_tier (capability floor) and implies candidates. It does not explain input_tokens or output_tokens, which are likely important for water estimation. Partial compensation for two of four parameters, so a 3 is appropriate.

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 clear, specific statement: 'Pick the least-thirsty candidate model that meets a capability floor.' This precisely identifies the action (pick), the resource (candidate models), and the qualifying condition (capability floor). It effectively distinguishes the tool from siblings like compare_models or estimate_water, which focus on estimation or comparison rather than selection.

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 explicitly says 'Use in agent graphs to route each step' and explains how to set min_tier, giving clear context for when to use it. It doesn't explicitly exclude alternatives or mention when not to use it, but the purpose is clear and aligns with its siblings. A small gap: it doesn't explicitly name sibling tools as alternatives, but the context implies it.

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