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.

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

With no annotations provided, the description carries the full burden of behavioral disclosure, and it discloses almost nothing: no confirmation that it is a read-only/non-mutating call, no auth requirements, no pagination or result-size behavior. The only hint is the word 'List', which weakly implies a safe read.

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 with no filler; every word earns its place and the resource is named before the qualifier.

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 simple no-argument list tool this is close to sufficient, but with no output schema and no annotations the description could reasonably state what the returned items look like (model names, aliases) or how they are consumed by jev_evaluate. It is adequate but leaves a small gap.

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?

The schema declares zero parameters at 100% coverage, so there is nothing for the description to compensate for; the baseline for a no-parameter tool applies.

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?

Specific verb (List) plus resource (Jev models and aliases) with a scoping qualifier ('available on this endpoint'), so an agent can immediately tell what the tool returns. It does not, however, differentiate itself from the sibling jev_evaluate or state what a 'model' means in this context.

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?

No guidance on when to call this versus jev_evaluate, no prerequisites, and no statement of typical workflow (e.g., list models first, then evaluate). Usage is only inferable from the verb 'List'.

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