List Models

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

Annotations already declare readOnlyHint=true and openWorldHint=false, and the description adds useful behavioral detail: it explicitly says the tool does not connect to the network and does not return keys. This is meaningful transparency beyond the structured annotations, though it does not elaborate on caching, freshness, or return shape.

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 compact sentence that front-loads the main purpose and appends key constraints after a semicolon. Every clause carries useful information with no redundant restatement of the tool name.

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 zero parameters and an output schema present, the description does not need to explain return values in detail. It covers the resource being listed and the important behavioral constraints, making the definition complete enough for an agent to call it 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 baseline for parameter semantics is 4. There is no parameter behavior to clarify, and the description appropriately does not invent any parameter semantics.

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 uses a specific verb (列出/list) and enumerates the exact resource contents: seven models with 2k/4k variants, API formats, access directories, and restrictions. This clearly distinguishes it from the sibling image-task tools, which submit, query, cancel, or batch-process images.

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 explains what is returned but gives no explicit guidance on when to use this tool, when not to use it, or which sibling to choose instead. The use case is implied by the name and contents, but no actual usage context or prerequisite is stated.

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