davinci-mcp

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Ai (write)

ai_write

Destructive

Runs AI-powered analysis, transcription, subtitle, speech, scene-cut, audio-classification, motion-blur, and reframe actions in DaVinci Resolve, verifying each change by read-back.

Instructions

Change ai state; may overwrite current values. Every change is read back from Resolve (receipt.verified=false means it did not take). op.action (params; * = required):

analyze_for_intellisearch(clip, folder, identify_faces, better_mode): Run Intellisearch analysis on a clip or folder (blocking, needs AI Extras).
analyze_for_slate(clip, folder, marker_color): Run Slate ID analysis on a clip or folder (job, needs the AI Slate ID Extras). [job]
create_subtitles_from_audio(language, caption_preset, chars_per_line, line_break, gap): Create subtitles from the audio of the current timeline (job). [job]
detect_scene_cuts(): Detect and make scene cuts along the current timeline (job). [job]
generate_speech(text*, voice_model, custom_voice_file, speed, variation, pitch, generation_id, filename, +2 more): Generate speech audio from text into the Media Pool (needs AI Speech Generator).
perform_audio_classification(clip, folder): Classify the audio of a clip or folder (blocking; works without Extras).
remove_motion_blur(clip, folder, options): Render motion-deblurred copies of a clip or folder's clips (blocking, creates clips).
smart_reframe(item*): Run Smart Reframe on a timeline item (job). [job]
transcribe_audio(clip, folder, speaker_detection, as_nested_clip): Transcribe the audio of a clip or folder (job; read it with get_transcription). [job] Shortcuts (prefer them for whole tasks):
workflow_write.build_vertical_reel: Build a vertical timeline from clips, add subtitles and optionally render it. One call instead of create_subtitles_from_audio. Calls known in advance (a sequence, the same change on several items): ONE batch_run.

Input Schema

Name	Required	Description
`op`	Yes	{action, ...params}. Parameters per action: see the description or this server's describe_action tool.
`dry_run`	No	true: return the planned effect, change nothing.
`snapshot`	No	true: back up the timeline/project first; omitted: server policy.

Output Schema

Name	Required	Description	Default
`job`	No
`plan`	No
`status`	Yes
`receipt`	No

Schema Changelog

Changes observed during successful MCP inspections.

Changed2 schema fields changedv1.0.4Nov 1, 2026
- addedInput schema / properties / dry_run / description
```
Added value: +"true: return the planned effect, change nothing."
```
- addedInput schema / properties / snapshot / description
```
Added value: +"true: back up the timeline/project first; omitted: server policy."
```
First observedv0.1.0Nov 1, 2026

TDQS

A4.8/5.0

Behavior5/5

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

Goes well beyond the annotations by warning that changes 'may overwrite current values' and by explaining the read-back receipt semantics ('receipt.verified=false means it did not take'). It also labels which actions run as blocking versus background jobs and notes side effects such as clip creation for remove_motion_blur.

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?

Front-loaded with the destructive warning and receipt semantics, then a tightly formatted per-action list. Dense but every line carries information; the only waste is the '+2 more' shorthand that partially defers rather than informs.

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

Completeness5/5

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

For a destructive, multi-action dispatcher this is comprehensive: atomic vs job behavior, Extras requirements, receipt verification, dry_run/snapshot semantics, and escalation to batch_run and describe_action. With an output schema present, return-value details are appropriately delegated.

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?

The schema allows additional properties in op, so the description carries the real parameter documentation, listing params per action with '*' marking required ones. Minor deduction for the '+2 more' truncation on generate_speech, which leaves two parameters to describe_action.

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?

Opens with a specific verb+resource ('Change ai state') and then enumerates every action the tool performs, each with a concrete description. An agent can distinguish this from ai_read, ai_delete, and workflow_write without opening any schema.

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

Usage Guidelines5/5

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

Explicitly routes the agent: prefer workflow_write.build_vertical_reel for whole tasks, use batch_run for known sequences, use get_transcription to read transcription results, and consult describe_action for parameter details. It also flags prerequisites per action ('needs AI Extras', 'works without Extras') and blocking vs job execution.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.