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

Annotations already declare readOnly, idempotent, non-destructive and closed-world, so the safety profile is covered. The description adds genuine behavioral context by naming the comparison inputs (announcements vs emissions/diversity/safety metrics) and explaining how to read the result ('a high gap score suggests potential inconsistency').

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?

Purpose is front-loaded in the first sentence, with output detail and the usage trigger following in a logical order. Slightly redundant to enumerate return fields when an output schema already exists, but nothing is wasted.

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 single-parameter analytical tool with an output schema and full annotation coverage, the description supplies what, why, output interpretation and when to use it. Minor gap: it doesn't note any precondition such as requiring prior announcements/ESG data for the ticker to yield a meaningful result.

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?

Only one parameter (ticker) with 100% schema description coverage, so the schema fully documents it. The description adds only the implicit constraint that the ticker is an NZX-listed company, which is the baseline expectation rather than a meaningful addition.

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 and resource ('Analyze ESG greenwashing risk for an NZX company') plus the mechanism ('compares sustainability claims in announcements against actual ESG metrics'). This clearly separates it from adjacent siblings such as get_esg_data, get_esg_disclosure_gap and get_climate_gap_analysis, which do not compare narrative against measured outcomes.

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?

Explicit trigger list: 'Use when asked about ESG claims, sustainability narrative, greenwashing, or environmental reporting consistency.' That gives a clear when-to-use context, but no when-not-to-use or named alternative for the overlapping ESG siblings.

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