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

Goes well beyond the annotations: discloses that a paid plan is REQUIRED, that one shared gateway operation is charged per call (not per model), and that an extra operation is spent only when both analyzeSentiment and highlightBrand are set. It also honestly warns that persona labels are not vendor-verified and that 'claude'/'perplexity' have no real vendor route.

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?

Three tightly-packed sentences, front-loaded with what it does, then the critical persona caveat, then the cost model. Every sentence earns its place with no filler.

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, the crucial interpretation caveat, cost/auth preconditions, and roughly what it returns ('each answer verbatim, with real citations'), which is sufficient given there is no output schema.

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?

With 86% schema coverage the schema carries most parameter detail, but the description adds non-obvious semantics: webSearch is shared across all personas, and the analyzeSentiment+highlightBrand combination triggers an extra charge. This meaningfully enriches the schema beyond field definitions.

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?

States a specific verb+resource ('Runs one prompt through up to 4 model personas') and scope ('returns each answer verbatim, with real citations when webSearch is on'), which cleanly distinguishes it from siblings like research_ai_prompts and get_direct_vendor_answers.

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?

Gives strong interpretive guidance ('treat every answer as a model answering as X, never as ground truth from X') and states the paid-plan precondition. It does not, however, explicitly name an alternative tool or the condition that selects this over research_ai_prompts, so it falls short of a 5.

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