FilmLab

Depth Map

lab_depth_map

Create or read a depth map for a project video in FilmLab: use it for fog, depth of field, relighting or occlusion. Near is white, like Resolve's Depth Map. Free on our own CPU, no credits; processing takes minutes. modell: v2 (default) is Depth Anything V2, sharpest edges. vda is Video Depth Anything: steadier over time (no brightness pumping across the shot, slightly less flicker), softer edges, about 2x slower than v2 (on our server ~0.5 s per frame for v2, ~1.1 s for vda), only for 16:9 landscape or 9:16 portrait videos (16:10 and 1.85:1 are fine, 4:3 and 2.39:1 are not). Each modell keeps its own map; always pass the same modell when reading again (engine in the answer says which one). Reuses the existing map or running job by default; neu: true starts a new job. Returns status and job_id; when done, tiefe_url is FFV1 gray16 and vorschau_url is a playable H.264 grayscale preview, both signed for download without a browser session. wait_s waits at most 15 seconds (default 0). If still running, call this tool again with the same project_id, video_id and modell, neu: false. Once done, use resolve_ergebnis_an_timeline with vorschau_url to put the preview as a matte above the original clip in Resolve.

Input Schema

TableJSON Schema

Name	Required	Description
`neu`	No	Start a new depth job instead of reusing the existing map or running job (default false)
`modell`	No	Depth engine: v2 (default, sharpest) or vda (steady over time, 16:9 or 9:16 only)
`wait_s`	No	Wait up to this many seconds for completion (default 0, maximum 15)
`video_id`	Yes	Completed video id belonging to this project
`nur_lesen`	No	Only read: never create a job. Without an existing job the answer is status 'keine' (default false)
`project_id`	Yes	Project id (from lab_list_projects)

Schema Changelog

Changes observed during successful MCP inspections.

Changed1 schema field changedOct 11, 2026
- addedInput schema / additionalProperties
```
Added value: +false
```

Changed1 schema field changedOct 10, 2026

addedInput schema / properties / modell

Added value: +{
+  "description": "Depth engine: v2 (default, sharpest) or vda (steady over time, 16:9 or 9:16 only)",
+  "enum": [
+    "v2",
+    "vda"
+  ],
+  "type": "string"
+}

First observedOct 3, 2026

TDQS

A4.8/5.0

Behavior5/5

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

Beyond the annotations (readOnlyHint=false, destructiveHint=false), it discloses cost ('free on our own CPU, no credits'), latency ('processing takes minutes', per-frame timings), the reuse-vs-new-job default via neu, and the format/signing of the returned URLs. This is unusually rich behavioral context.

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?

Purpose is front-loaded and almost every sentence carries distinct information (cost, timing, engine tradeoffs, read-back instructions). It is dense and slightly long, with mild repetition around passing the same modell when reading again, which keeps it from a 5.

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?

There is no output schema, but the description covers the return values in detail (status, job_id, tiefe_url as FFV1 gray16, vorschau_url as signed H.264 grayscale preview). Combined with the usage and behavioral detail, an agent has everything needed to call and follow up correctly.

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 description coverage is 100%, so the baseline is 3; the description goes further by expanding modell (engine identity, edge quality, temporal stability, 2x slowdown, aspect-ratio limits) and explaining neu and wait_s semantics. It adds real meaning over the schema rather than repeating it.

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 opens with a specific verb+resource ('Create or read a depth map for a project video in FilmLab') and immediately names the concrete uses (fog, depth of field, relighting, occlusion). It also names the downstream sibling resolve_ergebnis_an_timeline, so an agent can place it in a workflow without opening other schemas.

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?

It gives explicit when-to-use cases, selection guidance between the v2 and vda engines (with aspect-ratio constraints and speed tradeoffs), the default reuse behavior, and the exact re-call pattern ('call this tool again with the same project_id, video_id and modell, neu: false') when a job is still running.

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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