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

Annotations already mark the tool as read-only, open-world, idempotent, and non-destructive. The description adds valuable behavioral context beyond these: the default model (Workers AI Llama-3.3-70b, free), the fact that probing Anthropic requires a BYO API key and that the user pays Anthropic directly, and the per-model response structure. This discloses important side effects (external API calls with cost implications) not covered by annotations, with no contradiction.

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 primary action, and every sentence serves a purpose: what it does, special configuration/cost, and return format. There is zero redundancy or fluff, making it easy to parse.

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 having no output schema, the description explicitly lists the per-model return fields (score, confidence, signals, raw_response) and a combined view, covering the most critical return information. It also provides use cases and payment implications. It does not detail error handling or rate limits, but for a moderately complex probing tool with rich annotations, this is sufficient.

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 meaningful parameter semantics by explaining the default model when 'models' is omitted and clarifying that '_apiKey' is passed through to Anthropic and billed to the user. This goes beyond the schema's basic parameter descriptions, aiding the agent in choosing appropriate values.

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 opens with a specific verb ("Probe") and clearly identifies the resource (one or more LLMs) and the outcome (score visibility 0-100 per model). It distinguishes the tool from siblings like ask_pipeworx (a Q&A tool) and scan_competitor_ai_presence (which targets competitors), making its purpose unmistakable.

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 states clear use cases: "AI-marketing audits, pre-launch brand checks, competitive monitoring." This provides solid context for when to use the tool. However, it does not explicitly name alternatives or state when NOT to use it, so it stops short of full exclusionary guidance.

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