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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and non-destructive. The description adds genuinely useful behavioral context beyond those flags: the default model is free (Workers AI Llama-3.3-70b), probing Anthropic requires a BYO API key with direct payment, and the response shape is per-model {score, confidence, signals, raw_response} plus a combined view. It does not contradict annotations and gives important operational details.

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?

Three sentences, front-loaded with the core purpose, then operational details, then use cases. Every sentence adds value and there is no wasteful repetition of schema or annotation content. The structure is easy to scan and information-dense.

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 quite complete: it covers what the tool does, the default behavior, auth requirements, return format, and typical use cases. There is no output schema, but the description adequately explains the response. It could mention limitations (e.g., rate limits, response size) or explicitly differentiate from the closest sibling, but overall it is sufficient for an agent to invoke correctly.

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% (all parameters have descriptions), so baseline is 3. The description adds extra meaning by explaining the interplay between the models and _apiKey parameters — e.g., the default free model, that passing _apiKey enables Anthropic, and that users pay Anthropic directly. This goes beyond the bare schema by clarifying why and when to use specific parameters.

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?

Description opens with a specific verb ('Probe') and clear resource ('one or more LLMs') plus a defined output (visibility score 0-100 per model). It explicitly lists the entity types (business/brand/product/topic) and even previews the return structure, making the tool's function unmistakable and distinct from sibling tools like ask_pipeworx or deep_research.

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?

Provides clear, concrete use cases in the final sentence: 'AI-marketing audits, pre-launch brand checks, competitive monitoring.' This tells when to use the tool, but it does not explicitly mention when not to use it or compare it to alternative sibling tools like scan_competitor_ai_presence or compare_entities. Therefore it lacks explicit exclusions/alternatives, so it gets a 4.

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