Talk to your AutoWhisper CMO

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

With no annotations, the description carries full behavioral disclosure. It specifies important behavior: for adding a product, a URL is required, text-only descriptions fail, and placeholder/stock images are rejected. However, it does not mention potential destructiveness, rate limits, or authentication requirements, which would improve transparency.

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 moderately long but front-loaded with the primary action and examples. Every sentence adds value, though some repetition could be trimmed. It remains clear and informative without being verbose.

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?

Given no output schema, the description does not detail the reply format, but it states the tool returns a reply. It covers the main capabilities and usage scenarios. For a conversational AI tool, the description is fairly complete, though it could mention error handling or unsupported instructions.

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%, but the description adds significant meaning: instruction is natural language, product_id is optional, and the examples clarify usage. It also explains constraints for the 'Add product' case beyond the schema, enhancing parameter understanding.

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 sends a natural-language instruction to an AI CMO and returns its reply, listing specific capabilities (add product, generate content, etc.) and providing concrete examples. This distinguishes it from the sibling tool autowhisper_confirm, which likely handles confirmations.

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 explains when to use the tool (any natural-language instruction to the CMO) and gives examples. It does not explicitly compare to sibling tool autowhisper_confirm, but the context makes it clear that this is for direct instructions while the sibling likely handles confirmations. Lacks explicit 'when not to use' guidance.

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