Retrieve supported Wan 3.0 video generation models to choose a model before creating text-, image-, or reference-based videos.
Supported generation models.
This tool declares no required parameters. Add optional parameters from the input schema as needed.
{
"id": 1,
"jsonrpc": "2.0",
"method": "tools/call",
"params": {
"arguments": {},
"name": "list_models"
}
}| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Changes observed during successful MCP inspections.
v0.1.0Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and destructiveHint=false, so safety is covered, but the description adds nothing beyond that. It does not say whether the list is static or account-specific, whether it is paginated, or what each entry contains (IDs, display names, capabilities). For a discovery tool meant to feed other calls, this is a meaningful gap.
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?
It is a single fragment with no wasted words, but the brevity is under-specification rather than efficiency. It is not a complete sentence and is not front-loaded with the information an agent actually needs (what the list contains, how to use the results).
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 is the only place to learn what a returned model entry looks like and how to use it in later calls such as generate_video, yet it says nothing. For a simple zero-parameter read tool only a sentence or two is required, but those sentences are missing.
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 takes zero parameters and schema coverage is 100%, so there is nothing for the description to clarify. The baseline for a no-parameter tool applies, and no parameter claims are made that could mislead.
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?
"Supported generation models." is a noun fragment that essentially restates the tool name with a qualifier ("supported"). It gives no verb, no scope, and does not distinguish this tool from the sibling get_model, which presumably retrieves a single model. An agent can guess it lists models, but the definition does almost no work beyond the name.
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?
There is no guidance on when to call this versus get_model or how it relates to estimate_generation or generate_video. No prerequisites, no filters, no exclusions are mentioned. The agent must infer usage entirely from the name.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.