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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, destructiveHint. The description adds value by noting that the default call is free (Workers AI) and that Anthropic calls require a user-provided key (BYO key with direct payment). It also reveals the return shape (per-model fields + combined view), which is not in the 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 four sentences, each adding distinct information: core action, optional key, return structure, and use cases. No filler or repetition. Front-loaded with the verb 'Probe' and the key output (score 0-100).

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 no output schema, the description fully accounts for the tool's complexity: it covers all 4 parameters (entity, models, _apiKey, context), explains the return format (per-model objects with score, confidence, signals, raw_response plus combined view), and gives behavioral context (free default, BYO key for Anthropic). The 100% schema coverage complements this completeness.

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?

The input schema has 100% description coverage, so the baseline is 3. The description adds meaning beyond schema: it explains the default model when models is omitted, clarifies that _apiKey is only needed for Anthropic, and provides examples for entity (e.g., 'Pipeworx'). This extra context justifies a score of 4.

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 specifies a concrete action: probing LLMs for knowledge about an entity and scoring visibility. It names the default model (Workers AI Llama-3.3-70b) and the optional Anthropic provider, distinguishing this tool from siblings like ask_pipeworx or scan_competitor_ai_presence.

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 lists clear use cases (AI-marketing audits, pre-launch brand checks, competitive monitoring). It also explains when to pass _apiKey (Anthropic models) and the default model choice. However, it does not explicitly state when not to use this tool or compare to alternatives.

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