ffmpeg-mcp-video-editor

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Apply a LUT

apply_lut

Apply a .cube LUT to color grade a video. Pre-validates the LUT to prevent ffmpeg errors, and strength adjusts blending with the original.

Instructions

Apply a .cube colour lookup table.

The LUT is parsed and validated before the job is queued — declared size, row count and numeric content — because ffmpeg reports a malformed cube with an error that gives no hint what is wrong. Use 'strength' below 1.0 to blend the graded result back against the original.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "input_path": "<input_path>",
      "lut_path": "<lut_path>"
    },
    "name": "apply_lut"
  }
}

Input Schema

Name	Required	Description	Default
`encode`	No
`lut_path`	Yes	Path to a .cube LUT file.
`strength`	No	Blend against the ungraded image; 1.0 applies the LUT fully.
`input_path`	Yes	Path to the source media file.
`output_path`	No	Destination file. If omitted, the output is written into the job's workspace directory and its path is returned by job_result.
`interpolation`	No	Sampling mode. Tetrahedral is the usual choice for film LUTs.	tetrahedral

Output Schema

Name	Required	Default
`tool`	Yes
`job_id`	Yes
`status`	No	queued
`message`	No	Job queued. Poll job_status for progress, then job_result.

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Aug 6, 2026

TDQS

A3.8/5.0

Behavior4/5

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

The description discloses that the LUT is parsed and validated before the job is queued, and explains the reason (ffmpeg's unhelpful errors). It also explains the effect of 'strength' below 1.0. These details go beyond the schema and the annotations (which are all false and carry no safety meaning).

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 three sentences with no fluff. It front-loads the core purpose, then gives behavioral context about validation and a useful parameter tip. Every sentence 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?

With a rich input schema and an output schema present, the description doesn't need to explain parameters or return values. It adds key context about validation and job queuing. The only gap is the lack of explicit distinction from sibling color-grading tools, but overall it is complete enough for a job-based processing tool.

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

Parameters3/5

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

Schema description coverage is 83%, so most parameters are already well-documented in the schema. The description adds only a minor clarification on 'strength' which is essentially already captured in its schema description, so it provides no significant new parameter semantics.

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 'Apply a .cube colour lookup table', which is a specific verb+resource statement that clearly distinguishes it from siblings like apply_curves and color_grade. It immediately tells the agent the tool's exact function without ambiguity.

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

Usage Guidelines2/5

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

There is no explicit guidance on when to use this tool versus alternatives such as color_grade or apply_curves. The only usage-related tip is about the 'strength' parameter, which is parameter guidance rather than tool selection guidance.

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