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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint=false, so the description adds value by disclosing the return structure (score, confidence, signals, raw_response, combined view) and the cost implication of using Anthropic (BYO key, paid directly to Anthropic). This goes beyond the annotations without contradicting them.

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 three sentences, front-loaded with the core purpose, and every sentence provides essential information: function, output, model options, cost note, and use cases. No wasted 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?

Despite lacking an output schema, the description explicitly lists the per-model fields returned and the combined view. It covers use cases, default behavior, optional parameters, and payment implications. For a read-only, idempotent probe tool, this is comprehensive.

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 semantic meaning by indicating that the default model is Workers AI Llama-3.3-70b (free), that `_apiKey` is only required for Anthropic, and that `context` helps disambiguate common names. This enriches parameter understanding beyond the schema descriptions.

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 uses a specific verb ('Probe') and resource ('one or more LLMs') to clearly state the tool's function: assessing what LLMs know about a business/brand/product/topic and scoring visibility from 0-100. It distinguishes itself from siblings like ask_pipeworx (asking questions) and deep_research (in-depth research) by focusing on visibility auditing.

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.' It also explains when to use the `_apiKey` parameter (only if 'anthropic' is in models) and the default free model. It doesn't explicitly state when not to use it or mention alternatives, but the context is sufficient.

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