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

Annotations already declare read-only, idempotent, and non-destructive behavior, and the description adds context about the data source (observed telemetry) and a caveat about what the rankings are not. This exceeds the annotation baseline, though it does not disclose return format, data freshness, or whether results are historical or real-time.

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, the core purpose is front-loaded, and every phrase adds meaning. The phrase 'free neutral' is slightly ambiguous but not redundant.

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 three undocumented parameters and no output schema, the description is too terse. An agent would not know what 'job' or 'category' mean, nor what the return object looks like. Annotations handle safety, but parameterization and output expectations are 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%, so the description must compensate. It indirectly hints at 'variant' by naming reliability and latency, but 'job' and 'category' are completely unexplained, leaving a significant gap for an agent to correctly set all parameters.

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 clearly states a specific verb ('ranks') and resource ('endpoints'), and differentiates itself by noting the ranking is not payment or buyer-demand data. However, it does not explicitly contrast with sibling tools like reliability_quality_benchmark_data_feed, so it lacks full sibling differentiation.

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 implies a usage context (ranking endpoints by reliability, latency, confidence) but provides no explicit when-to-use or when-not-to-use guidance. It does not name alternative tools or exclusion conditions, which is a gap given the large family of related sibling tools.

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