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

Annotations already establish read-only, idempotent, non-destructive behavior. The description adds key behavioral contours: it returns IDs only, in alphabetical order, restricted to currently registered models, and not full model objects or metadata—no surprises.

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 sentence where each word earns its place: verb, resource, scope, and ordering are all present with no filler or redundancy.

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 zero-argument read-only list endpoint, the description fully covers what an agent needs to know: what is returned (IDs), the population (all currently registered models), and ordering (alphabetical). Nothing essential is missing, and no output schema or annotations are contradicted.

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?

There are zero parameters and schema coverage is trivially 100%. With no inputs, the description has nothing else to add beyond what the schema already conveys, meeting the baseline for parameterless tools.

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?

States a precise verb ('List'), resource ('Valem models'), scope ('currently registered'), and ordering ('alphabetical'). Clearly differentiates from sibling tools like get_model_info, which targets a single model, and create/delete/patch, which are mutations.

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 makes the call context explicit: it is for enumerating all currently registered model IDs, so an agent needing just IDs will choose this tool. It does not explicitly name alternatives, but for a zero-parameter list tool the context is clear and there are no exclusions.

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