List AI Models

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

No annotations are provided, so the description carries the full burden. It discloses the information payload (pricing, context window, speed) and data source, but does not mention pagination, rate limits, or any side effects. The 'List' verb implies a read-only operation, though this is not explicitly stated.

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 two short, front-loaded sentences with no redundancies. It states the action, key fields, and source in a compact format, earning high marks for conciseness and structure.

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?

With no output schema, the description names the key fields (pricing, context window, speed) to convey the return structure. It omits pagination defaults and sorting, but for a simple list tool with optional limit/offset, these are minor gaps. The source context adds completeness.

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 input schema covers both parameters (limit, offset) with clear descriptions and defaults, so the description need not add parameter-level detail. The description adds no semantics beyond the schema, matching the baseline for 100% schema coverage.

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 clearly states the action ('List AI model catalog') and identifies the specific data included (pricing, context window, speed). This distinguishes it from sibling tools like get_model, which likely retrieves a single model, and search_tools, which searches the general tool catalog.

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 a browsing use case ('List AI model catalog') and the source (SWEN.AI benchmark database), but does not explicitly state when to use this tool versus alternatives like get_model. There are no exclusions or alternative guidance, but the purpose is clear enough for basic usage.

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