Skip to main content
Glama

video_ai_color_grade

Apply color grading to a video using a LUT file, a style preset (e.g., cinematic, vintage), or by matching the color balance of a reference video.

Instructions

Apply a color grade to a video — by LUT file, style preset, or reference video.

Args: input_path: Video file to grade. output_path: Where to write the graded video. reference_path: Optional reference video — when given, the video's color balance is adjusted to match the reference (overrides style). style: Style preset. One of: auto (gentle contrast lift), warm, cool, vintage, cinematic, dramatic, noir (high contrast, desaturated). lut_path: Optional .cube/.3dl LUT file applied with FFmpeg lut3d — overrides both reference and style for professional grading looks.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
styleNoauto
lut_pathNo
input_pathYes
output_pathYes
reference_pathNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior3/5

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

Discloses that LUT is applied with FFmpeg lut3d and that reference triggers color balance matching, but no annotations are provided and additional behavioral details (e.g., overwrite policy) are absent.

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

Conciseness5/5

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

The description is concise, well-structured with a docstring-style Args section, and front-loads the main purpose without unnecessary words.

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?

Covers input parameters and their interactions; output schema exists so return values are not needed. Minor omissions like performance constraints do not significantly detract.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

With 0% schema description coverage, the description fully compensates by explaining all five parameters, including style preset options and the precedence between reference and LUT.

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 clearly states that the tool applies a color grade to a video using three methods (LUT, style, reference), distinguishing it from other video tools in the sibling list.

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?

Describes the three methods and their precedence (reference overrides style, LUT overrides both), providing clear guidance on when to use each, though lacking explicit comparison to non-grading video tools.

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

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/KyaniteLabs/kinocut'

If you have feedback or need assistance with the MCP directory API, please join our Discord server