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

Annotations already declare readOnly, idempotent, non-destructive. The description adds that it makes external API calls and that Anthropic calls incur cost paid directly to Anthropic, which is above and beyond annotations. It also describes the return structure.

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 four sentences with no fluff. It opens with the core action and return format, then covers model options, cost details, and use cases. Perfectly concise and well-structured.

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 well (per-model {score, confidence, signals, raw_response} + combined view). It covers parameters and use cases sufficiently, though details on 'signals' or 'confidence' are missing but not critical.

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%, so baseline is 3. The description adds context that Workers AI is free and Anthropic requires BYO key, which provides value beyond schema. However, the schema already describes parameter purposes sufficiently.

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 it probes LLMs about a business/product/topic and scores visibility (0-100). The verb 'probe' and resource 'LLMs' are specific, and the tool is distinct from siblings 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 explains when to use (AI-marketing audits, pre-launch brand checks, competitive monitoring) and provides details on default model vs. Anthropic (BYO key). However, it does not explicitly state when not to use or compare to alternatives, so it misses some guidance.

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