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text_to_speech

Turn written text into natural-sounding audio using ElevenLabs models via RunAPI, returning task status and audio file URLs.

Instructions

Create a ElevenLabs task on RunAPI (text to speech). Returns a task id, status, and output URLs.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
textYes
waitNoPoll until the task reaches a terminal status.
modelNoRunAPI model slug for this model line.
speedNo
styleNo
voiceNo
next_textNo
stabilityNo
timeout_msNo
timestampsNo
callback_urlNo
language_codeNo
previous_textNo
poll_interval_msNo
similarity_boostNo
Behavior2/5

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

With no annotations provided, the description carries the full burden of disclosing behavior. It mentions that the tool returns a task id, status, and output URLs, hinting at asynchronous behavior, but does not explain the task lifecycle, polling requirements, or any side effects. This is minimal and insufficient for a task-based tool.

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

Conciseness4/5

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

The description is a single concise sentence that gets to the point quickly. It is efficient, though the grammar is slightly awkward ('Create a ElevenLabs'). It earns a high score for brevity.

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

Completeness2/5

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

Given the tool's complexity (15 parameters, async task, no output schema, no annotations, multiple siblings), the description is far from complete. It only provides a high-level overview and omits critical details about how to use the tool, what the required 'text' parameter does, and how to handle returned task IDs.

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

Parameters2/5

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

Schema description coverage is only 13% (2 of 15 parameters have descriptions). The description adds no parameter meaning, leaving the many parameters (e.g., voice, speed, style, stability) unexplained. It does not compensate for the low schema coverage.

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 action ('Create'), the resource ('ElevenLabs task on RunAPI'), and the domain ('text to speech'). It does not explicitly differentiate from sibling tools like text_to_dialogue or text_to_sound, but the core purpose is unambiguous.

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?

No guidance is given on when to use this tool versus alternatives. The description does not mention contexts, exclusions, or prerequisites. It only states what the tool does, leaving the agent to infer usage.

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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