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generate_audio_track

Generate AI audio and immediately place it on the edit audio timeline. Returns both the MediaDocV2 ID and the audio track ID.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
modelNoAudio model ID
editIdYesThe edit ID
promptYesTTS/music/SFX prompt
volumeNoInitial volume
voiceIdNoVoice ID for TTS models
durationNoPlaceholder timeline duration until generation metadata updates
startTimeNoTimeline start time in seconds
fadeInTimeNoFade-in length in seconds
autoChannelNoPick the first non-overlapping channel automatically
fadeOutTimeNoFade-out length in seconds
stylePromptNoDelivery and accent direction for supported speech models
workspaceIdYesThe workspace ID
pinnedToShotIdNoOptional shot ID to pin this audio to
relativeStartTimeNoOffset in seconds from pinned shot start

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

B3.3/5.0
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 behavioral burden. It reveals that this is a mutating generation-and-placement operation and that IDs are returned, but it omits permissions, asynchronous generation behavior, effects on existing timeline audio, and failure modes for a 14-parameter write tool.

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 two tightly structured sentences with no redundancy. The core action is front-loaded, and the return-value note is secondary.

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?

With no annotations and no output schema, the description helpfully states the return IDs, but it is thin for a complex 14-parameter mutation tool. It does not provide enough behavioral or usage context for an agent to know when and how to invoke it confidently against alternatives.

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

Parameters3/5

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

Schema description coverage is 100%, so the input schema already explains all 14 parameters. The description adds no parameter-level detail beyond noting that IDs are returned, making the baseline 3 appropriate here.

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 gives a specific verb and resource: 'Generate AI audio' and 'place it on the edit audio timeline.' The 'immediately place' phrase distinguishes it from siblings like generate_audio and add_audio_track, so an agent can identify its role without opening the schema.

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?

It implies use when an agent needs generated audio placed directly on the timeline, but it never states when to choose this over generate_audio, generate_video_audio, or add_audio_track, nor does it give exclusions or prerequisites.

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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