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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and no destructive behavior. The description adds useful behavioral context: the default model is free, Anthropic requires BYO key, and the tool returns 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 a single paragraph of 3 sentences, each conveying important information: purpose, model options, return structure, and use cases. It is front-loaded and avoids unnecessary detail, though bullet points could improve readability.

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 no output schema, the description explains the return format (per-model score, confidence, signals, raw_response + combined view). It covers main functionality but omits potential error scenarios (e.g., invalid API key, model unavailability). The annotations mitigate some need for behavioral description. Overall adequate for the tool's complexity.

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%, but the description adds value beyond the schema by explaining the default model ('Workers AI Llama-3.3-70b (free)'), clarifying the _apiKey parameter's purpose, and providing examples for the entity parameter. This helps the agent understand parameter usage better.

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 knowledge about a business/topic and scores visibility (0-100). It specifies the default model and optional Anthropic probe, distinguishing it from sibling tools like ask_pipeworx or deep_research which serve different purposes.

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 provides clear use cases (AI-marketing audits, pre-launch brand checks, competitive monitoring) and explains when to use the optional _apiKey parameter. However, it does not explicitly contrast with sibling tools or mention when not to use this tool.

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