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

Annotations already declare readOnlyHint=true, and the description adds meaningful context beyond this: it notes the default model is free, that passing `_apiKey` triggers direct calls to Anthropic with cost implications for the user, and it discloses the response structure (per-model {score, confidence, signals, raw_response}). This adds transparency about external calls and costs that annotations alone do not provide.

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 compact—three sentences—with the main purpose front-loaded. Every sentence contributes new information: what it does, the default model and cost nuance, the return format, and use cases. There is no redundancy or filler.

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?

The description is highly complete for a read-only tool with no output schema. It explains the output shape, default behavior, optional external API usage, cost implications, and real-world use cases. Given the tool's moderate complexity and annotations that cover safety, the description leaves no significant gaps.

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 the baseline is 3. The description adds value by explaining the default model (Workers AI Llama-3.3-70b) and the conditional need for `_apiKey` only when probing Anthropic, which is not evident from the schema alone. This clarifies parameter relationships and defaults.

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's function with a specific verb ('probe') and resource ('one or more LLMs'), and explains the visibility scoring (0-100) per model. It also distinguishes from sibling tools by emphasizing per-model visibility scores and specific models like Workers AI and Anthropic, which sets it apart from general ask or research 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 provides clear context for when to use the tool: 'AI-marketing audits, pre-launch brand checks, competitive monitoring.' It also explains the default model and optional Anthropic probing with a BYO key, giving practical usage guidance. However, it does not explicitly name alternatives or exclusion criteria when other tools might be more appropriate.

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