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

readOnlyHint=true already tells the agent this is a safe read, and the description usefully frames it as a predictive/simulation call ('would pick') rather than an action. It adds no detail on rate limits, output shape, or whether the prompt is stored, which is minor for a pure-read classifier.

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?

Two short sentences, purpose front-loaded, no filler. Every clause contributes either what it does or when to use it.

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?

For a single-parameter, read-only tool with no output schema, the description covers the essentials: what is classified and what is returned (task class and model tier). It could state the output format or whether this is a dry-run that never dispatches, but the gap is small.

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% and there is a single well-documented parameter, so the schema carries the load. The description adds marginal meaning by scoping the input to a 'coding prompt', implying non-coding prompts are out of domain, but nothing beyond that.

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 names a specific verb ('Shows') and a concrete resource ('which task class and model tier the jev-auto router would pick'), so the agent knows exactly what it returns. It is clearly distinct from siblings like list_model_aliases or get_client_setup, though it never names them explicitly.

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 gives a use case ('to explain or predict routing decisions') which implies when the tool is appropriate. However, there are no exclusions, no prerequisites, and no comparison against the sibling tools that could plausibly be confused with it.

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