list_models

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

No annotations are present, so the description carries the full behavioral burden. It does add real value by disclosing the return shape ('one line per model: id | type | capabilities | price'), which is not in any schema. It omits anything about ordering, pagination, or read-only/auth characteristics.

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 earning its place: purpose, filters, and return format. The core purpose is front-loaded and nothing is repeated from the schema verbatim without added meaning.

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 two-optional-parameter list tool with no output schema, the description is nearly self-sufficient: it names the filter axes and describes the returned line format. Only minor gaps remain (ordering, result limits, whether an empty filter returns everything).

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%, which sets a baseline of 3, but the description goes beyond it: it decodes the ambiguous enum values ('speech for text-to-speech, transcription for speech-to-text') and explains the query semantics ('case-insensitive match on model id or name'). That is genuine semantic uplift over the raw schema.

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 specific verb ('List') and resource ('AI Gateway models this server can call'), and the filtering dimensions make clear this is a discovery/catalog tool. This cleanly distinguishes it from every sibling, which are all generation or file-manipulation actions.

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 tool's role and its two filter dimensions clear, so usage is implied (discover available models before invoking generate_*). However, it never states when to reach for this versus an alternative, nor any prerequisite such as needing a resolved model id before generation.

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