Look up RunAPI pricing for ElevenLabs model endpoints. Specify a model slug and action to see text-to-speech or text-to-dialogue costs before running tasks.
Look up RunAPI pricing for the elevenlabs model line.
| Name | Required | Description | Default |
|---|---|---|---|
| model | No | Model slug. Defaults to the line's primary model. | |
| action | No | Endpoint name. Defaults to the endpoint that offers the model. |
Changes observed during successful MCP inspections.
v0.3.0Input schema / properties / action / enumPrevious value: -[
- "isolate_audio",
- "speech_to_text",
- "text_to_dialogue",
- "text_to_sound",
- "text_to_speech"
-]New value: +[
+ "text_to_dialogue",
+ "text_to_speech"
+]v0.2.0Input schema / additionalPropertiesRemoved value: -falseInput schema / properties / model / enumRemoved value: -[
- "audio-isolation",
- "speech-to-text",
- "text-to-dialogue-v3",
- "sound-effect-v2",
- "text-to-speech-multilingual-v2",
- "text-to-speech-turbo-v2.5"
-]v0.1.0Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure, yet it only says it looks up pricing. It does not state that the operation is read-only, whether it requires authentication, or what the return format contains, leaving key behavioral traits unstated.
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, front-loaded sentence with no wasted words. It efficiently communicates the tool's purpose and scope without unnecessary elaboration.
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?
There is no output schema, so the description should explain what pricing data is returned, but it does not. Combined with no annotations and no usage guidance, the definition is too sparse for an agent to understand the tool's full behavior and return values.
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 description coverage is 100%, so the schema already documents both the 'model' and 'action' parameters, including an enum for action. The description adds no parameter-level detail beyond the scope phrase 'elevenlabs model line,' so the baseline of 3 is appropriate.
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 states a specific verb ('Look up') and resource ('RunAPI pricing') with a clear scope ('elevenlabs model line'). It does not explicitly differentiate itself from sibling tools like text_to_speech or text_to_dialogue, but the pricing focus makes its purpose distinct.
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 use this tool versus alternatives, no prerequisites, and no mention of related tools such as text_to_speech or text_to_dialogue. The description only states what it does, leaving usage context entirely implied.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.