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luquimbo

DaVinci Resolve MCP Server

by luquimbo

clip_transcribe_audio

Transcribe a clip's audio into text. Provide the clip name to start speech-to-text transcription in DaVinci Resolve.

Instructions

Start audio transcription for a clip.

Requires DaVinci Resolve 19+ with speech-to-text support enabled.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
clip_nameYesName of the clip to transcribe.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior2/5

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

No annotations are provided, so the description must carry the full disclosure burden. It only discloses the version/feature requirement, but omits critical behavioral aspects such as whether transcription runs asynchronously, whether the tool returns immediately, what errors occur if the clip is invalid or unsupported, and whether the transcription is stored persistently. This is minimal and leaves significant uncertainty.

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 short sentences. The first sentence immediately states the action and target; the second adds a necessary prerequisite. There is no fluff or redundant information. It is front-loaded and efficient.

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?

Despite having an output schema, the description is short and does not explain the transcription workflow. It does not mention that starting transcription is likely asynchronous or that the transcript can be retrieved later with clip_get_transcript. For an action-oriented tool, this missing contextual guidance makes it incomplete for an agent to know how to use it effectively in a multi-step process.

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?

The input schema has 100% coverage with clip_name described as 'Name of the clip to transcribe.' The tool description only says 'for a clip' and adds no additional meaning beyond the schema. Since the schema fully documents the parameter, the baseline score of 3 is appropriate.

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 the tool's action and target: 'Start audio transcription for a clip.' This is a specific verb + resource combination that distinguishes it from siblings like clip_get_transcript (which retrieves) and clip_clear_transcript (which clears). The tool name reinforces the purpose.

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

Usage Guidelines3/5

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

The description provides a prerequisite ('Requires DaVinci Resolve 19+ with speech-to-text support enabled'), which tells the agent when the tool is usable. However, it does not explicitly mention when to prefer this tool over alternatives like media_transcribe or what to do after starting (e.g., use clip_get_transcript to retrieve results). This leaves usage context mostly implied.

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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