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

Annotations already provide readOnlyHint, idempotentHint, openWorldHint, destructiveHint. The description adds useful behavioral context: default model is free, API key is passed directly to Anthropic (user pays), and returns structured per-model data. No contradiction with 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 front-loaded with core purpose, followed by model details, API key usage, return structure, and use cases. Every sentence adds value, and it is compact without unnecessary 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?

Given the tool's complexity (multiple models, optional API key) and no output schema, the description sufficiently covers return fields (per-model score, confidence, signals, raw_response + combined view). It provides all necessary context for an agent to use the tool effectively.

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 clarifying that 'entity' includes brand/product/person/topic, 'models' array accepts specific values, '_apiKey' is only needed with 'anthropic', and 'context' disambiguates common names. This goes beyond 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 clearly states the tool probes multiple LLMs for brand/product visibility and scores it 0-100 per model. It specifies the verb 'probe' and resource 'LLMs', and distinguishes from sibling tools like entity_profile or deep_research by focusing on AI visibility measurement.

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 explains when to use (AI-marketing audits, pre-launch brand checks, competitive monitoring) and clarifies the default model vs. requiring an API key for Anthropic. However, it does not explicitly state when not to use or contrast with specific siblings.

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