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

Annotations already declare readOnly, openWorld, idempotent, non-destructive. The description adds meaningful context beyond annotations: cost implications ('you pay Anthropic directly'), the free default model, and the exact return structure. This enriches the behavioral profile without contradicting 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?

Three sentences, each earning its place: purpose, model options/cost, and return shape. Front-loaded with the core function; no filler or repetition.

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-param tool with no output schema, the description is complete. It specifies the return format (per-model object + combined view), covers all parameters implicitly, and adds useful cost/behavioral context. The schema handles parameter details, so nothing critical is missing.

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 baseline 3 applies. The description does not add meaning beyond what the schema already provides for each parameter; it simply mirrors the default model and optional key behavior already documented in the 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: 'Probe one or more LLMs for what they know... and score visibility (0-100) per model.' It clearly distinguishes itself from siblings like scan_competitor_ai_presence by focusing on general visibility scoring rather than competitor-specific analysis.

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 gives clear use contexts ('AI-marketing audits, pre-launch brand checks, competitive monitoring') and explains model selection (default workers-ai, optional anthropic with BYO key). It doesn't name explicit alternatives like scan_competitor_ai_presence, but the use cases are sufficient.

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