Check AI visibility

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

While annotations already mark the tool as readOnlyHint, the description adds valuable behavioral context: it returns trend data from stored snapshot history, includes a name_warning caveat that must be relayed, and discloses the subscription requirement. It also tells the agent how to handle the warning (relay it, offer to re-run). This goes well beyond the annotations.

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 average but every sentence provides necessary information: purpose, user intents, return values, warnings, and subscription context. It is front-loaded with the main purpose and follows with structured details. A slight deduction for density, but no wasted words.

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 tool with no output schema, the description fully explains what the agent will get: a score, per-engine breakdown, top competitor, and trend data. It also covers the name_warning edge case and the subscription prerequisite, making the tool's context complete enough for correct invocation and result interpretation.

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 input schema already provides 100% coverage with detailed descriptions for each parameter, including the caveats about business_name and business_location. The tool description does not add additional parameter-level meaning beyond what the schema already states, so the baseline of 3 is appropriate.

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 the tool's function: checking how visible a website is to AI assistants. It provides specific example queries ('does ChatGPT/Perplexity/Claude/Gemini recommend this business') that anchor the purpose, and the focus on AI visibility distinguishes it from sibling tools like run_audit or get_benchmark.

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?

Explicitly says 'Use this whenever someone asks...' with a list of concrete user intents. It also gives an exclusion/alternative: if the user lacks a subscription, call get_sample_audit instead. This provides both when-to-use and when-not-to-use guidance, satisfying the dimension fully.

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