Capture One AI Windows

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adjust

Applies one qualified numeric or structured adjustment module to Capture One, using the exact module token, target layer, and observation token.

Instructions

Apply one qualified numeric or structured module using its exact observed module token.

Structured modules: curves {channel,points:[[x,y],...]}; color_mixer {mode:'basic',color,hue?,saturation?,lightness?} or {mode:'advanced'|'skin',action:'add'|'update'|'remove',rgb? ,index?,hue?,saturation?,lightness?,smoothness?,uniformity_hue?,uniformity_saturation?,uniformity_lightness?,enabled?,inverted?}; levels {channel,input_black?,input_white?,midtone?,output_black?,output_white?}; color_balance {wheel,x?,y?,lightness?}; layers {action,layer_ref?,creation_operation_id?,opacity?}; masks {action:'create_subject'|'create_background'}. Layers also support rename {layer_ref,name}, set_enabled {layer_ref,enabled}, duplicate {layer_ref,name?}, move {layer_ref,index}; index is zero-based excluding Background. Masks on the selected adjustment layer: invert, clear, fill {density:0..1}, copy {source_layer} replaces the entire mask; combine {source_layer,mode:add|subtract|intersect}; feather {radius:0..300}; linear_gradient {start:[x,y],stop:[x,y],midpoint:0.01..0.99}; radial_gradient {center:[x,y],radius_x,radius_y,falloff:0..1,angle:-360..360,inverted:bool}; luma_range {shadow,shadow_falloff,highlight,highlight_falloff,radius,sensitivity,inverted}. Gradient coordinates/radii use normalized RAW dimensions, not rotated viewer coordinates. Luma endpoints 0..255 ordered shadow_falloff<=shadow<=highlight<=highlight_falloff, radius 0..300, sensitivity 0..100. select_submask {index} uses the native indexes returned by masks read; -1 selects the whole mask. Brush/gradient operations follow C1's selected submask semantics. Luma range replaces existing luma components, preserving the other masks. Each color module also accepts {action:'reset'} (C1 defaults) or {action:'paste',source_observation_token} (a saved complete read of the same module). Read the destination module before applying; source snapshot may belong to another layer/photo. Native qualification remains required. No presets or style files are created. Scalar parameters, levels, all mixers, color wheels and curves share the C1 internal model endpoint for Background and the selected adjustment layer. Select a layer then re-read the module; target.layer binds every write to that layer. Native AI masks use AutoMaskingManager without requiring the tool panel to be visible. Levels endpoints use 0..255, midtone -1..1. Advanced H/S/L limits are +/-30, -100..200, +/-100; Skin +/-5, +/-30, +/-10. Smoothness 1..30; Skin uniformity 0..100. Advanced/Skin may also update hue_low/high and saturation_low/high: signed offsets from the source color, low<=-2, high>=2, hue span<=359 degrees. Skin has one range. Read gives current native ranges and indexes; no screenshot or UI color picking is required.

Input Schema

Name	Required	Description	Default
`patch`	Yes
`module`	Yes
`target`	Yes
`request_id`	No
`observation_token`	Yes

Output Schema

Name	Required	Description	Default
No arguments

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Oct 28, 2026

TDQS

A3.9/5.0

Behavior4/5

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

With no annotations, the description carries the full behavioral burden and does disclose substantial semantics: module patch shapes, mask replacement behavior, target.layer binding, token binding, and that no presets or style files are created. It still does not clearly state reversibility/undo behavior or general permission/failure characteristics.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The purpose is front-loaded and the detail is largely relevant to a complex tool, but the description is a dense wall of semicolon-separated jargon. It would be much easier to scan with structured grouping by module or parameter.

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?

Given the complex nested schema, zero schema description coverage, and no annotations, the description is unusually complete about supported modules and patch structures. Some top-level parameter and workflow gaps remain, notably request_id and the precise meaning of 'qualified'.

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 0%, so the description must compensate, and it does explain much of module, patch, observation_token, and target.layer semantics in detail. However, it does not define the top-level target shape fully and never mentions the optional request_id parameter.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The first sentence states a specific verb and resource: apply one qualified numeric or structured module using an observed module token. It is clear this is a mutation/adjustment tool, but it does not explicitly distinguish itself from the sibling read or operations tools beyond saying to read the destination module first.

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 gives a clear prerequisite workflow: read the destination module before applying, select a layer and re-read, and note that native qualification remains required. It does not name alternatives or spell out when not to use this tool, which keeps it from a 5.

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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