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list_models

Retrieve all available text-to-speech models from ElevenLabs to select the appropriate voice synthesis option for your audio generation needs.

Instructions

List all available models

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Implementation Reference

  • The handler function for the 'list_models' tool, decorated with @mcp.tool for registration. Fetches models from ElevenLabs API and converts them to McpModel instances.
    @mcp.tool(description="List all available models")
    def list_models() -> list[McpModel]:
        response = client.models.list()
        return [
            McpModel(
                id=model.model_id,
                name=model.name,
                languages=[
                    McpLanguage(language_id=lang.language_id, name=lang.name)
                    for lang in model.languages
                ],
            )
            for model in response
        ]
  • Pydantic BaseModel defining the output schema for models returned by list_models tool.
    class McpModel(BaseModel):
        id: str
        name: str
        languages: list[McpLanguage]
  • Pydantic BaseModel defining the schema for language objects within McpModel.
    class McpLanguage(BaseModel):
        language_id: str
        name: str
Behavior2/5

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the action ('List all available models') but doesn't describe any behavioral traits such as pagination, rate limits, authentication requirements, or what 'available' means (e.g., filtered by permissions). This is a significant gap for a tool with zero annotation coverage.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, efficient sentence with no wasted words. It is appropriately sized for a simple tool and front-loads the key information, making it easy to parse quickly.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's simplicity (0 parameters, no output schema), the description is minimally adequate but lacks context. It doesn't explain what 'models' refers to (e.g., AI models, voice models) or provide any output details, which could be helpful since there's no output schema. This leaves gaps in understanding the tool's scope and results.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The tool has 0 parameters, and the schema description coverage is 100% (though empty). The description doesn't need to add parameter semantics, so it meets the baseline of 4 for tools with no parameters, as it doesn't introduce confusion or omissions regarding inputs.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the verb ('List') and resource ('all available models'), making the purpose unambiguous. However, it doesn't distinguish this tool from its siblings (like 'list_agents' or 'list_conversations'), which would require specifying what type of models it lists or in what context.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides no guidance on when to use this tool versus alternatives. It doesn't mention any prerequisites, exclusions, or related tools (e.g., whether this is for AI models, voice models, or another type), leaving the agent to infer usage from the tool name alone.

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

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