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

Annotations already cover safety (readOnly, idempotent, non-destructive, closed-world). The description adds real behavioral context beyond them: it discloses the output categories and a methodological caveat that lifecycle footprints are 'not like-for-like.' This is useful framing an agent would not get from annotations alone.

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?

A single front-loaded sentence leading with the verb and required unit, followed by the output categories. The long enumerations are dense but each maps to a real return value; only slightly wordy.

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 no output schema, the description must convey what comes back, and it does — the comparison categories plus the AI-prompt count and the like-for-like caveat. For a one-parameter read-only conversion tool, nothing needed to invoke it correctly is missing.

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 sole parameter title is a bare 'Water Ml', so the description carries the burden. It explicitly names the unit ('a volume of water (mL)'), resolving the only semantic ambiguity for the required input.

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?

States a specific verb+resource ('Convert a volume of water (mL) into everyday comparisons') and enumerates the exact comparison categories returned. The scope (mL input, everyday-context output) is distinct from the estimate_* siblings, so an agent can tell them apart without opening schemas.

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?

Usage is implied by the purpose (you have a water volume and want it in relatable terms), but the description never says when to choose this over estimate_ai_footprint, especially since it also returns 'number of typical AI text prompts' — an overlap that could route an agent to the wrong sibling. No explicit when/when-not guidance.

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