list_models

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 conveys a read-only list operation, but adds no details about output format, ordering, performance, or side effects. For a simple list tool this is minimally adequate, but it does not go beyond the literal action.

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 a single short sentence that is front-loaded with the key action and resource. Every word contributes meaning, with no redundant content.

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 simplicity of the tool (no parameters, no output schema), the description fully states what the tool does. It does not explain return value format, but for a list operation, the implied return is the collection of models, which is sufficient in this context.

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 tool takes zero parameters, and the schema is empty with 100% coverage. Per the rubric, the baseline for 0 params is 4. The description does not need to explain any input semantics since none exist.

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 "List all downloaded Ollama models" uses a specific verb ("list"), identifies the resource ("downloaded Ollama models"), and specifies the scope ("all"). This clearly distinguishes it from sibling tools like show_model (which likely displays a single model) and ask_model (which likely queries a model).

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 implies its use case—simply list all downloaded models—but provides no explicit guidance on when to choose this over alternatives. It does not mention show_model or ask_model as alternatives or exclusions, leaving the decision to the agent.

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