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

With no annotations, the description carries full burden. It discloses missing fields behavior, follow-up next_question, API key requirement, and AI budget usage. No contradictions. Could add clarity on whether any state is modified, but overall transparent.

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 concise sentences: core purpose, missing field behavior, and integration/dependency. Every sentence adds value; no redundancy or 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?

Given one input and no output schema, the description covers input style, output behavior (missing fields, next_question), and integration with mu_rfq_create. It mentions spec_id implicitly. Could explicitly state output format, but it's adequate.

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% for the single parameter 'prompt'. The description adds an example and clarifies the type of input (free-text request for manufacturing spec), providing context beyond the schema's brief description.

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 it converts natural language into a structured manufacturing spec, listing fields (kind, material, dimensions, etc.). This distinguishes it from siblings like mu_rfq_create, which use the resulting spec_id.

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 instructs to feed the resulting spec_id into mu_rfq_create, and notes the prerequisite of an API key (register/verify first). This provides clear when-to-use guidance and connects to a sibling tool.

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