Retrieve all available text-to-speech models to choose the right one for your speech generation needs.
Get all available text-to-speech 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": "elevenlabs_get_models"
}
}| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Changes observed during successful MCP inspections.
v0.1.1Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the burden of behavioral disclosure. The word 'Get' implies a read-only operation, but the description does not explicitly state safety characteristics, side effects, or return format. It is sufficient for a simple fetch but lacks explicit behavioral context.
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, clear sentence that directly states the purpose with zero fluff. It is front-loaded and perfectly sized for the tool's simplicity.
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?
While the tool is extremely simple and the description covers its core function, the absence of an output schema means the description should ideally hint at what is returned. 'All available text-to-speech models' implies the response contains model data, but additional detail about the return structure would improve completeness. Given the simplicity, the current description is still adequate.
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 description has nothing to add beyond the schema. Per rubric, 0 params gives a baseline of 4. The description does not introduce any parameter-related inconsistency.
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 clearly states the action ('Get') and the resource ('all available text-to-speech models'), making the tool's purpose unambiguous. It is easily distinguished from sibling tools that deal with voices or TTS operations.
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 does not explicitly provide when-to-use guidance or mention alternative tools. However, the sibling tool names (elevenlabs_list_voices, elevenlabs_get_voice_info, etc.) imply this tool is specifically for listing models, so usage is contextually implied but not explicitly stated.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.