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

Annotations already declare readOnlyHint, idempotentHint, non-destructive, and closed-world. The description adds useful context about the data returned (32 roles, three usage levels, best global and Chinese models, capability floor), but does not describe response structure, pagination, or any rate/auth behavior.

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?

A single, front-loaded sentence that packs the scope, usage levels, and model-selection criteria without any filler. Every clause adds useful information.

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?

There is no output schema, so the description should carry more of the return-value burden. It explains the core output content (monthly token usage and cost, best models) and scope, and annotations cover safety, but it leaves group and limit semantics unexplained and does not describe the response format.

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 description coverage is only 25%. The description mentions 32 job roles and light/standard/heavy usage, which partially helps interpret the query and usage parameters, but it says nothing about the group categories or the limit parameter. With two of four parameters left undocumented, the description does not compensate for the low schema coverage.

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 resource and scope: monthly token usage and cost for 32 common job roles at three AI usage levels, along with best model and best Chinese model meeting a capability floor. It does not explicitly differentiate from siblings such as estimate_enterprise_ai_cost or cheapest_model_for_score, but the job-role focus is specific enough.

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?

There is no guidance on when to use this tool versus alternatives, when not to use it, or any prerequisite context. Usage is only implied by the description of what it returns.

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