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

Annotations indicate read-only, open-world, non-destructive behavior. The description adds context beyond annotations: returns numbered citations, supports specific filter types, and notes speed/cost trade-offs. It does not contradict annotations, and the added behavioral details are valuable for expectation-setting.

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?

Every sentence earns its place: purpose, use cases, output format, performance, filters, and alternatives. It is front-loaded and clearly structured, with no wasted words despite covering multiple facets.

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?

Given the tool's moderate complexity, the presence of an output schema, and rich annotations, the description covers all essential aspects: operation, output, configuration, and tool differentiation. It is fully sufficient for an agent to select and invoke the tool correctly.

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%, so the parameter semantics are already documented. The description restates filter types (recency, domain, context size) but does not meaningfully enrich beyond the schema's own descriptions. Baseline 3 is appropriate because the schema carries the burden.

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 specific verb and resource: 'Answer a question using web-grounded AI (Sonar Pro model).' It clearly contrasts with sibling tools by naming perplexity_research and perplexity_reason, making the tool's specific role unmistakable.

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?

Provides explicit 'Best for' scenarios (quick factual questions, summaries, explanations, general Q&A), performance expectations ('Fastest and cheapest'), and direct alternatives ('For in-depth multi-source research, use perplexity_research instead'). This gives agents clear when-to-use and when-not-to-use guidance.

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