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

Annotations already cover the safety profile (readOnly, idempotent, non-destructive, not open-world), and the description adds value by disclosing the shape of the returned analytics (percentile, mean, top failures). However, it does not explain how the cohort is determined given that no sector/country/domain parameter exists, which is the key behavioral unknown for a benchmark tool. Some credit beyond annotations, but the central mechanism stays opaque.

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?

Two sentences, front-loaded with the resource and its contents, then the usage triggers. No filler and no repetition of schema or annotation content.

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 zero-required-parameter analytics tool with no output schema, the description gives a reasonable account of what comes back. But it never resolves how a specific domain or sector is identified, since neither appears in the input schema, so an agent cannot predict what cohort it will actually be benchmarked against. That is a material gap for a comparison tool.

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% and both parameters are enums with described values, so the schema carries the parameter burden. The description adds no meaning about how 'format' affects output or how 'profile' scopes the benchmark cohort. Baseline 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?

States a specific verb and resource ('Get industry benchmark data') and enumerates what the response contains: percentile rank within a sector/country cohort, mean score, and common DNS security failures. It is clear about the tool's subject, though it does not distinguish itself from related analytics siblings like get_domain_rank or get_provider_insights. Minor friction: it frames the benchmark around 'a domain's security score' when the schema only accepts a profile, not a domain.

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?

Explicitly lists three triggering questions (how a score compares to the industry average, what percentile a score is in, what the most common failures are in an industry). That is a clear when-to-use signal. It does not name when NOT to use it or point to alternatives such as get_domain_rank, leaving the agent to infer routing.

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