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

Annotations already declare readOnlyHint=true and destructiveHint=false, so the description need not repeat safety traits. It adds valuable behavioral context: the credit cost, the free condition for known-empty lookups, the 'one row per domain' return structure, and the country-specific data availability (ChatGPT vs Google AI Overviews). This goes beyond structured fields and helps the agent set expectations.

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 three concise sentences, front-loading the purpose and then providing cost and return information. Every sentence earns its place: purpose, cost, return. No fluff or repetition of schema details.

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 4-parameter tool with no output schema, the description covers the essential aspects: what it does, what it returns, and cost. It does not explain the confirm_quote_id flow (though schema does) or detail the output row structure beyond listing metrics. Given the absence of an output schema, the description is reasonably complete but could specify the row fields more explicitly.

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?

The schema has 100% parameter description coverage, so the baseline is 3. The description does not add additional parameter-specific meaning beyond summarizing them ('your domain against up to 4 competitors'). It mentions the output but not parameter nuances, so it does not elevate beyond the already-rich schema.

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+resource: 'AI mentions of your domain against up to 4 competitors'. It enumerates the exact metrics returned (mentions, share, AI search volume, per-platform split), clearly distinguishing it from siblings like ai_check (single-domain) or ai_trend (trends). An agent can infer this is a comparative analysis tool without opening schemas.

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 usage for comparing a domain against competitors in one country, and adds cost information (4 credits, free when no domains are cited). However, it does not explicitly contrast with sibling tools or state when not to use it. The comparative framing is clear enough to guide selection, but lacks explicit exclusions or alternative references.

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