verify_recommendation

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

Annotations already declare the tool read-only, idempotent, and non-destructive. The description goes beyond this by explaining that it flags suspect recommendations, returns per-item evidence, and conditionally runs corroboration searches across independent journalism and tech sources when a claim is provided. This adds meaningful behavioral context beyond the annotation hints.

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 longer than minimal but every sentence adds useful context: what it audits, what it returns, when corroboration triggers, and how it fits with sibling tools. The opening sentence front-loads the core purpose, and the structure flows logically from behavior to purpose to related tools.

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 output schema exists, the description does not need to detail return formats. It covers input scope, optional behavior, output evidence types, purpose, and sibling tool integration. An agent has enough information 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 baseline is 3. The description adds narrative context around the claim parameter's effect on corroboration searches and frames the recommendations array as the core input, but it does not add significant detail beyond what the input schema already documents.

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 states a specific verb ('Audit'), a clear resource ('an AI recommendation list'), and enumerates the exact evidence dimensions returned, such as self-promotion patterns, conflicts of interest, and domain reputation. It also distinguishes itself from generic search tools by naming its purpose: catching GEO and brand-favoring listicles.

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 clearly implies when to use this tool: when auditing a recommendation list for manipulation signals. It also explicitly says to use it alongside web_search and verify_citation for auditing sources and claims. However, it does not explicitly state when not to use it or name specific alternative sibling tools for exclusion.

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