AI and analysis operations on the current timeline. Actions: create_subtitles(settings?) -> {success} — auto-caption from audio detect_scene_cuts() -> {success} analyze_dolby_vision(clip_ids?, analysis_type?) -> {success} grab_still() -> {success} grab_all_stills(source?) -> {count}
AI agents invoke timeline_ai to trigger actions in DaVinci Resolve MCP. What it does depends on the arguments the agent supplies, and its effects often reach beyond the immediate call — builds kicked off, notifications sent, workflows started.
This tool triggers multiple automated AI and analysis operations on the timeline — including auto-captioning, scene cut detection, Dolby Vision analysis, and frame grabbing. These are active operations that modify or augment the timeline (e.g., adding subtitles, markers) and trigger external compute processes, placing it firmly in Execute.
From the tool's definition AI and analysis operations on the current timeline: create_subtitles (auto-caption from audio), detect_scene_cuts, analyze_dolby_vision, grab_still, grab_all_stills — these trigger external processing operations and automated workflows within DaVinci Resolve
Documented attack patterns abuse exactly the kind of access timeline_ai gives an agent:
PolicyLayer is an MCP gateway — it sits between your AI agents and DaVinci Resolve MCP, and nothing reaches the server without passing your rules. This is the rule we recommend for timeline_ai:
{
"version": "1",
"default": "deny",
"tools": {
"timeline_ai": {
"limits": [
{
"counter": "timeline_ai_rate",
"window": "minute",
"max": 10,
"scope": "grant"
}
]
}
}
}timeline_ai stays usable, but rate-capped — a runaway agent can't fire it dozens of times a minute. Everything else on the server is denied unless you say otherwise.
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AI and analysis operations on the current timeline. Actions: create_subtitles(settings?) -> {success} — auto-caption from audio detect_scene_cuts() -> {success} analyze_dolby_vision(clip_ids?, analysis_type?) -> {success} grab_still() -> {success} grab_all_stills(source?) -> {count}. It is categorised as a Execute tool in the DaVinci Resolve MCP MCP Server, which means it can trigger actions or run processes. Use rate limits and argument validation.
Register the DaVinci Resolve MCP server in PolicyLayer and add a rule for timeline_ai: allow, deny, rate-limit, or require approval. Point your MCP client at the PolicyLayer proxy URL and the rule is enforced on every call, before it reaches DaVinci Resolve MCP. Nothing to install.
timeline_ai is a Execute tool with high risk. Execute tools should be rate-limited and have argument validation enabled.
Yes. Add a rate_limit block to the timeline_ai rule in your PolicyLayer policy. For example, setting max: 10 and window: 60 limits the tool to 10 calls per minute. Rate limits are tracked per agent session and reset automatically.
Set action: deny in the PolicyLayer policy for timeline_ai. The AI agent will receive a policy violation error and cannot call the tool. You can also include a reason field to explain why the tool is blocked.
timeline_ai is provided by the DaVinci Resolve MCP server (samuelgursky/davinci-resolve-mcp). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
Deterministic rules across all 369 DaVinci Resolve MCP tools. Per-identity grants. Full audit log. Live in minutes. Nothing to install.
Free to start. No card required.
369 DaVinci Resolve MCP tools catalogued and risk-classified — across an index of 42,500+ MCP servers.