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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and no destructiveness. The description adds value by explaining the return structure (per-model {score, confidence, signals, raw_response} + combined view) and cost implications for Anthropic. It does not contradict annotations and provides behavioral context beyond what annotations convey.

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?

Two sentences: the first covers the main action and output, the second adds details and use cases. Every phrase is necessary; no fluff. The description is front-loaded with the most critical information.

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?

Despite no output schema, the description adequately explains return values. All 4 parameters are documented in the schema and the description adds practical context. For a read-only probe tool with clear annotations, this description covers all necessary aspects an agent needs to invoke it 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 coverage is 100% with descriptions for all 4 parameters. The description adds meaning by explaining the default model for workers-ai, how _apiKey is passed through, and that context helps disambiguate. This goes beyond the schema's 'description' fields.

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 probes LLMs for visibility of an entity and returns a score. It distinguishes from sibling tools by specifying 'score visibility (0-100) per model' and provides use cases like AI-marketing audits and competitive monitoring, differentiating it from general search or entity profile tools.

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 explicitly recommends use cases (AI-marketing audits, pre-launch brand checks, competitive monitoring) and explains the default model vs. paid Anthropic option. It does not explicitly contrast with sibling tools like compare_entities or scan_competitor_ai_presence, but the guidance is clear enough for an agent to decide when this tool is appropriate.

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