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

While annotations cover technical safety (readOnlyHint: true), the description adds essential operational behavioral traits—namely, that the output is informational and lacks production guarantees. This is vital context for an agent interacting with real-world manufacturing logistics.

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 two sentences are highly efficient: the first states the tool's utility (what it checks), and the second states its limitations (what it does not confirm). No sentence is superfluous.

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 that it is a read-only, zero-parameter tool with clear operational constraints defined in the description, the definition is highly complete for an AI agent to use it responsibly.

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?

Since there are zero parameters, the description cannot add semantic meaning beyond what is already implied by the zero-parameter input schema. The baseline score of 4 is appropriate for this scenario.

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 defines the tool's function as an information retrieval mechanism regarding requirements, minimum orders, and quoting process. This distinguishes it clearly from sibling tools like 'submit_patch_inquiry'.

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?

It explicitly states crucial limitations by declaring that it does not confirm production availability or deadlines. This serves as a strong usage guideline, preventing the agent from making assumptions based on the tool's output.

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