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 value beyond them by stating the response contains 'the guidance with the supporting numbers' — the only signal about return shape, since no output schema exists.

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 clause and the second sentence covers the return. The category enumeration is dense but functional; nothing is padding, though the list could be trimmed without loss.

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 one-optional-parameter read-only lookup with full annotation coverage, the description supplies purpose, triggering intents, and a hint at the response payload. Only the default behavior when no agent_need is provided is left unaddressed.

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 coverage is 100% and the single parameter is optional with its own examples, so the schema carries the semantics. The description's category list roughly mirrors the parameter's example values but adds no format, granularity, or default-behavior detail (e.g., what happens when agent_need is omitted).

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 question the tool answers ('Which web search API fits an agent') grounded in a named benchmark, and enumerates the query intents it covers. It implicitly distinguishes itself from compare_providers by framing the output as a recommendation rather than a comparison, though it never names that sibling explicitly.

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 enumerated intent list (voice/chat turns, deep research, semantic/long-tail queries, docs lookups, domain use cases) gives concrete conditions for when this tool is the right pick. It stops short of saying when to use compare_providers instead, so the boundary between the two is left to inference.

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