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Clueso

by io.clueso

Set voice

set_voice

Set the voiceover voice for every clip in a project.

Use this to apply a clueprint's voice (read voiceover.voice.name and voiceover.voice.engine from the clueprint source data), or to switch all clips to a specific voice in one call. The voice is looked up by name + engine; lookup is case-insensitive on the name.

Common engines: 'eleven' (ElevenLabs), 'cartesia', 'google'.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
project_idYesThe project (guide) ID
voice_nameYesVoice name as stored in the voices table (e.g. 'Alex', 'Sofia')
voice_engineYesVoice engine — 'eleven', 'cartesia', 'google', etc.

TDQS

A4.4/5.0
Behavior4/5

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

Annotations already show destructiveHint=false and readOnlyHint=false, so the agent knows it is a mutation that is not destructive. The description adds that it affects every clip, is case-insensitive on name, and lists common engines, which provides useful behavioral context beyond annotations.

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 concise with three short sentences, each serving a distinct purpose: stating the action, providing usage context, and listing examples. There is no wasted text, and key information is front-loaded.

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

Completeness4/5

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

Given the tool's simplicity (3 required parameters, no output schema), the description is mostly complete. It could briefly mention the return value or confirmation behavior, but the context signals indicate low complexity, so the description suffices.

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?

Schema coverage is 100% with clear descriptions for each parameter. The description adds value by clarifying that voice_name lookup is case-insensitive and by listing common engine examples, but these are enhancements rather than necessities since the schema already documents the parameters adequately.

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

Purpose5/5

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

The description clearly states it sets the voiceover voice for every clip in a project. It distinguishes between applying a clueprint's voice and switching all clips to a specific voice, differentiating it from sibling tools like voiceover_batch or update_clips.

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

Usage Guidelines4/5

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

The description provides explicit use cases: applying a clueprint's voice or switching all clips to a specific voice. It gives guidance on how to read voice data from clueprints, but does not explicitly state when not to use it (e.g., for individual clip voice changes) compared to alternatives like update_clips.

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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TDQS

A4.1/5.0
Disambiguation4/5

Most tools have clearly distinct purposes, but there is notable overlap between remove_elements and remove_from_project(target='element'), which both remove elements from a clip. This duplication could cause an agent to misselect. Otherwise, tools like add_clips, add_elements, add_audio, and analyze_audio are well-differentiated.

Naming Consistency5/5

All tools follow a consistent verb_noun pattern (e.g., add_clips, create_project, get_clip, update_elements). There are no camelCase or mixed conventions. Even compound names like voiceover_batch and auto_sync fit the pattern. This makes the tool set predictable for an agent.

Tool Count2/5

With 40 tools, the set is significantly larger than the 3-15 range that typically earns its place. While the domain of video creation is broad, several tools seem redundant (remove_elements vs remove_from_project) or narrowly scoped (get_design_guide, get_element_schema), inflating the count. The number feels heavy for the apparent scope.

Completeness4/5

The tool surface covers most lifecycle operations: create, read, update, delete for projects, clips, elements, audio, articles, and clueprints. Minor gaps exist, such as no explicit tool to delete a voiceover (only mute via update_clips) and no folder management beyond listing. Overall, agents can accomplish full workflows with few workarounds.

Resources