list_models

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

No annotations provided, so description carries full burden. It transparently describes a read-only listing operation with no destructive behavior. Could mention idempotency or rate limits, but not critical for a simple list tool.

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?

Two sentences: first states core purpose, second provides usage guidance. No wasted words, highly efficient and 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?

Given low complexity (1 optional param, no output schema), description covers what the tool returns (models and video constraints) and when to use it. Could mention that the list is filtered by provider, which is implied. Almost 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 0% and parameter 'provider' has no description in schema. Description adds meaning by stating 'per provider (Agnes / Zhipu)', giving concrete examples. However, it does not specify exact allowed values or format, leaving some ambiguity.

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?

Description clearly states 'List available AI image/video models and video constraints per provider', specifying the action (list) and resource (models and constraints). It also distinguishes from sibling generation tools like generate_image and create_video by noting it is used to discover model names before calling them.

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?

Explicitly says 'Use to discover model names... before calling generate_image / create_video', providing clear when-to-use context. Does not explicitly state when not to use, but the purpose is sufficiently clear and functional.

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