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

Annotations already indicate read-only, idempotent, and non-destructive behavior. The description adds valuable context: probing multiple LLMs, scoring details, that Anthropic requires a BYO key and direct payment, and the return structure (score, confidence, signals, raw_response per model plus combined view). No contradiction with 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 a single paragraph of three sentences, front-loaded with the core function, followed by the key behavioral option (Anthropic), and ending with return structure and use cases. Every sentence adds new information with no fluff.

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 documents the return format ('per-model {score, confidence, signals, raw_response} + a combined view') sufficiently for the tool's complexity (4 parameters, no enums or nested objects). Combined with the schema, an AI agent has all needed information to select and invoke 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?

Schema coverage is 100% with each parameter described. The description enhances understanding by giving concrete examples (e.g., entity: 'Pipeworx', context: 'Boston restaurant') and explaining the default model and the optional Anthropic probe via _apiKey, which provides practical usage guidance 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 clearly states the tool probes LLMs for knowledge about an entity and scores visibility 0-100 per model, with specific verb 'Probe' and resource 'LLMs'. It distinguishes its purpose from siblings by mentioning AI-marketing audits, pre-launch brand checks, and competitive monitoring.

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 use cases ('AI-marketing audits, pre-launch brand checks, competitive monitoring') and explains when to use the default model vs. Anthropic with a key, but does not explicitly state when not to use or compare to alternative sibling tools.

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