list_models

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

With no annotations, the description carries the full behavioral burden. It reveals a non-obvious behavior: some IDs returned are internal names that generation endpoints reject, and explains the dotted alias convention. This is meaningful behavioral disclosure beyond a simple 'list' statement.

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?

Three sentences, each serving a clear purpose: stating the resource, connecting it to sibling tools, and providing a vital troubleshooting caveat. No filler or repetition, and the most important usage context is front-loaded.

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 parameterless listing tool with no output schema and no annotations, the description is complete: it defines the resource, the modalities, the intended downstream use, and the known edge case. An agent has everything needed to invoke and interpret the result correctly.

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 has zero parameters, so the schema is trivially complete. The description correctly omits parameter details and instead focuses on output semantics and caveats, which is the appropriate use of description space for a parameterless tool.

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 explicitly that it lists model IDs from apimodels.app across specific modalities (chat, image, video, audio), and frames the purpose as supplying IDs for the sibling tools. This clearly distinguishes it from the generation tools in the sibling list.

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?

Directly instructs the agent when to use the tool: before calling other tools, using returned IDs as inputs. The caveat also gives actionable guidance on handling invalid IDs by trying the dotted variant, which is precisely when-to-use-and-what-to-do guidance.

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