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

The annotations already define the tool as safe (`readOnlyHint: true`) and constrained (`openWorldHint: false`). The description provides no additional critical behavioral constraints (like regional pricing differences or latency implications), making a baseline score appropriate.

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 perfectly sized and front-loaded. It explains the core function and the input logic in a single, efficient sentence, avoiding unnecessary fluff.

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?

Despite having 8 highly complex parameters, the description successfully provides a high-level functional context. It guides the AI on how to simplify the complexity via presets, ensuring the agent understands the tool's practical utility in matching cost to workload.

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?

Given that the input schema coverage is 100%, the schema itself fully documents all 8 parameters. The description's added value is low, merely framing the purpose of the numerical inputs rather than adding technical meaning beyond the schema.

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 core action ('Rank models by monthly cost') and the target ('a workload'). It successfully distinguishes this function from cost estimation or simple model listings by framing the output as a ranking based on workload parameters.

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 provides immediate and actionable guidance by offering two distinct paths: using a provided workload preset (chatbot, rag, etc.) or manually supplying granular token numbers. This effectively tells the AI how to decide which parameters to populate.

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