jev_evaluate

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

With no annotations, the description carries the full burden of behavioral disclosure. It notes that English works best and that model defaults to jev-latest, but does not state whether the tool is read-only, what permissions or costs apply, how errors are handled, or what side effects exist.

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 dense but front-loaded, starting with the core action before detailing parameters. Every sentence carries relevant information, though the compact single-paragraph format could be structured more readably for complex nested types.

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 the lack of annotations, output schema, and any schema descriptions, the description adequately covers input parameters but does not explain the return shape of 'calibrated answers' or provide usage context. It is sufficient for forming a call but leaves gaps around output interpretation and tool selection.

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 0%, so the description must compensate, and it does substantially. It defines state as string or JSON, explains the questions object with question names, type options (noul, choice, score), instructions, and criteria formats, and states the model default. Some nested criteria semantics remain slightly cryptic, but the coverage is strong.

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 states a specific verb and resource: evaluate one text (state) against typed questions with Jev and return calibrated answers. It is clear what the tool does and how it differs from the sibling jev_models, though it does not explicitly name or contrast the sibling.

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?

The description explains input structure but gives no explicit guidance on when to use this tool versus alternatives, nor any when-not conditions or prerequisites. Usage is only implied by the evaluation purpose.

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