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

Even with annotations already declaring readOnly/openWorld/idempotent, the description adds valuable behavioral detail: the default model (Workers AI Llama-3.3-70b) is free, using Anthropic requires a BYO key with direct payment, and the response structure is outlined. It also mentions how _apiKey is handled ('passed straight through'), which is important for cost and privacy expectations.

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, each earning its place: function and output, default model and optional Anthropic integration, return format and use cases. It is front-loaded with the most important information and contains zero 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?

With no output schema, the description explicitly states the return structure (per-model {score, confidence, signals, raw_response} + combined view). It also covers model options, cost, use cases, and required parameter (entity). For a 4-param tool with no output schema, this is remarkably complete.

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 already covers all parameters at 100% coverage, so the baseline is 3. The description enriches this by explaining the default model behavior and the 'BYO key' payment implication for _apiKey, adding meaning beyond the schema's basic descriptions. This lifts it slightly above the baseline, though the schema still does most of the heavy lifting.

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 resource ('one or more LLMs'), then defines the output (visibility score 0-100 per model). It clearly distinguishes itself from sibling tools like scan_competitor_ai_presence by focusing on individual LLM awareness scoring rather than a broader competitor scan. The scope (business/brand/product/topic) is explicit.

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 gives explicit use cases: 'AI-marketing audits, pre-launch brand checks, competitive monitoring.' It does not state when not to use it or name alternatives, so it stops short of full exclusionary guidance. However, the context is clear and actionable enough for an agent to select this tool appropriately.

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