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

Annotations already declare readOnly=True, idempotent=True. Description adds that default model is free Workers AI, while Anthropic requires BYO key with direct payment, and returns per-model scores and combined view. No contradiction.

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?

Two sentences, front-loaded with main action, each sentence adds meaningful detail without redundancy. Efficient text.

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?

Covers purpose, parameters, return structure, and usage scenarios. Lacks mention of error handling or timeout behavior, but is sufficient for a non-mutation tool with good annotations.

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 descriptions. Description adds value by explaining default model and the _apiKey requirement for Anthropic, and purpose of context parameter for disambiguation.

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?

Description clearly states the tool probes LLMs for visibility scoring on a business/brand/product/topic, with specific verb 'probe...score'. It distinguishes from sibling tools like ask_pipeworx (Q&A) or deep_research (general research) by focusing on AI visibility audits.

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?

Explicitly lists use cases: 'AI-marketing audits, pre-launch brand checks, competitive monitoring'. Does not mention when NOT to use or alternative siblings, but context makes intended usage clear.

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