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

Annotations already declare readOnlyHint=true, destructiveHint=false, idempotentHint=true, and openWorldHint=true, making the tool's safety profile clear. The description adds valuable context: it details the probing mechanism, per-model scoring, the need for a personal API key for Anthropic, and the return structure. No contradictions 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 five sentences, front-loaded with the core purpose and followed by parameter details and use cases. Every sentence adds value without redundancy. It is concise and well-structured.

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?

For a tool with 4 parameters (1 required), 100% schema coverage, no output schema, and read-only annotations, the description is complete. It explains the return format (per-model score, confidence, signals, raw_response + combined view), parameter dependencies, and use cases. No gaps are evident.

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 description coverage is 100%, so the schema already documents all parameters. The description adds further meaning: it clarifies 'entity' as a brand/business/product name, explains the supported models and their API key requirements, and describes the 'context' parameter as a disambiguation aid. This exceeds baseline expectations.

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 a business/brand/product/topic and scores visibility (0-100) per model. It specifies the default model and optional Anthropic probe, distinguishing it from sibling tools like 'ask_pipeworx' or 'deep_research' which serve different purposes. The verb 'probe' and resource 'LLMs for visibility' are specific.

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 explicitly lists use cases: AI-marketing audits, pre-launch brand checks, competitive monitoring. It also guides when to use the _apiKey parameter (for Anthropic probing). However, it does not explicitly state when not to use the tool or compare it to alternatives among the many siblings, but the context is clear enough for an agent to infer appropriateness.

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