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

The description discloses that the tool makes external LLM calls, including a BYO-key Anthropic path where the user pays Anthropic directly, which is beyond the annotations. It also reveals the return shape: per-model {score, confidence, signals, raw_response} plus a combined view. This aligns with the read-only and open-world hints and adds actionable context.

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 action, then adds cost and return details, and ends with use cases. No redundant phrases; every sentence provides distinct value.

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?

Given there is no output schema, the description carries the full burden of explaining returns, and it does so with a concrete object shape. It specifies required input, optional model selection, key handling, and applicable use cases. This is complete enough for an agent to invoke the tool 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?

The input schema already covers all parameters with detailed descriptions (100% coverage), including the default model and the `_apiKey` requirement. The description repeats the default model and key condition but does not add any new semantic detail beyond the schema, so it stays at the schema-driven baseline of 3.

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 the specific verb 'probe' and clearly defines scope: 'what they know about a business / brand / product / topic' and the output 'score visibility (0-100) per model.' This distinguishes it from siblings like ask_pipeworx (internal knowledge) and scan_competitor_ai_presence (competitor-specific focus). The title and description align well.

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 concrete use cases: 'AI-marketing audits, pre-launch brand checks, competitive monitoring.' It also explains the default model and the BYO-key condition for Anthropic, which guides invocation. However, it does not explicitly name alternative tools or state when not to use this tool, so it lacks full exclusion guidance.

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