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

Annotations already indicate read-only/idempotent behavior; the description adds valuable context about cost (BYO Anthropic key, paid directly) and default model choice, which goes beyond the structured annotations. No contradiction is present, and the external API interaction is openly disclosed.

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, and every sentence contributes distinct information: action/scope, model options/cost, and output/use cases. No wasted words.

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?

Although there is no output schema, the description explicitly lists the return shape ({score, confidence, signals, raw_response} + combined view). It covers inputs, defaults, cost, and use cases, giving the agent enough context to invoke the tool correctly without needing additional details.

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 enhances this by explaining the default model (Workers AI Llama-3.3-70b), clarifying that `_apiKey` is only needed for Anthropic, and implying the behavior of the `models` parameter. This adds practical meaning beyond the schema.

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 a specific verb ('probe') and resource ('LLMs'), and clearly defines the output (visibility score 0-100). It distinguishes itself from sibling tools like ask_pipeworx or scan_competitor_ai_presence by focusing on AI knowledge scoring rather than general Q&A or competitor scanning.

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) which indicate when to use it. However, it does not explicitly name alternative tools or mention when not to use it, so it stops short of a full 5.

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