list-models

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

With no annotations, the description carries the burden of behavioral disclosure. It does add useful context: filters are combined with AND, active_only defaults to true, and search is case-insensitive. However, it does not mention pagination, result limits, or the exact shape of the response, which are relevant for a list endpoint.

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 compact, with the main purpose front-loaded in the first sentence and filter details structured in the second. The second sentence is dense but organized with backticks and examples. Overall, every sentence provides useful information without padding.

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 no output schema, the description should give some indication of return structure. It mentions returned fields ('upload constraints, supported media types, and features') but does not describe pagination, ordering, or how to handle empty results. For a list tool with multiple filter options, this is adequate but not fully complete.

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%, so a baseline of 3 applies. The description adds value beyond the schema by explicitly stating that all filters are optional and combined with AND, and by summarizing the scope buckets and search semantics. This helps the agent assemble correct queries without reading deep into each schema property.

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 ('List') and resource ('AI models') and elaborates on the returned details ('upload constraints, supported media types, and features'). It is clear but does not explicitly distinguish from sibling tools like list-ai-services or get-model-info, leaving some ambiguity.

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 intended use is implied: to list AI models with optional filters. However, there is no explicit guidance on when to choose this tool over alternatives (e.g., list-ai-services for services, get-model-info for a single model). The context is clear but exclusions or alternative conditions are not stated.

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