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Measure a Clip (Scopes)

lab_scopes
Read-only

Measure ONE clip of the edit as numbers instead of a picture: a frame is rendered through the clip's export path (input profile, grade LUT, secondary correction) and measured — luma percentiles (P1/P50/P99, Rec.709), mean R/G/B, green excess (G minus the mean of R and B: >0 green cast, <0 magenta), vectorscope centroid (Cb, Cr), clipped highlights and crushed shadows. befund says what is off in words with the number; vorschlag.grade is a correction computed on a sample of the source through the clip's full grade (gamma wheel master first, then exposure, temperature, tint, gain wheel) towards median luma 0.42 and no cast WITHOUT pushing highlights into clipping, with the values it expects — pass it to lab_grade_clip, then measure again. When nothing more is gained, vorschlag.grade is null and grund says why. Give clip_id (from lab_grade_clip without grade) or zeit_s (seconds on the timeline); default is the middle of the clip. Writes nothing, costs nothing.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
seq_idYesSequence id — positive integer as string
zeit_sNoTimeline position in seconds (picks the clip there; within a clip_id: the frame)
clip_idNoClip to measure

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changed
    • addedInput schema / additionalProperties
      Added value: +false
  2. Added

TDQS

A4.3/5.0
Behavior4/5

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

Annotations declare readOnlyHint=true, and the description reinforces this with 'Writes nothing, costs nothing.' Beyond that it discloses substantive behavior: the frame is rendered through the clip's export path (input profile, grade LUT, secondary correction) and the correction is computed on a sample of the source through the full grade toward median luma 0.42 without pushing highlights into clipping. This is rich context the annotation alone does not convey.

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 purpose and efficient overall, but the middle sentence is a dense, jargon-heavy run-on (befund, vorschlag, grund interleaved with grading parameters) that would scan faster as a short list. Every sentence still earns its place.

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

Completeness4/5

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

No output schema exists, so the description carries the return-value burden and largely meets it, explaining befund, vorschlag.grade, grund, and the measured quantities. The stopping condition is covered; the exact output field structure could be spelled out further, keeping it just short of fully complete.

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 adds genuine meaning beyond the schema: clip_id comes 'from lab_grade_clip without grade', zeit_s is seconds on the timeline, and the default with neither is the middle of the clip — details absent from the schema.

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?

States a precise verb+resource+scope: 'Measure ONE clip of the edit as numbers instead of a picture,' and specifies exactly what is measured (luma percentiles, mean R/G/B, green excess, vectorscope centroid, clipping). It also implicitly distinguishes itself from the write-side sibling by directing the resulting vorschlag.grade to lab_grade_clip.

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

Usage Guidelines4/5

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

The description lays out the intended workflow loop: pass vorschlag.grade to lab_grade_clip, then measure again, and stop when vorschlag.grade is null. This is clear context for when to use it, though it never explicitly names an alternative measurement tool (e.g. lab_mix_messen) or states when not to use it.

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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