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Valmera — AI Video Editor

by io.valmera
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Import lut

import_lut

Validates and applies a CUBE lookup table from a project asset or URL over a program-time window, with configurable strength and a maximum supported grid size.

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": {
      "project_id": "<project_id>"
    },
    "name": "import_lut"
  }
}

Input Schema

NameRequiredDescriptionDefault
endNoprogram s
urlNo
startNoprogram s
strengthNo
asset_keyNo
project_idYesExplicit project identifier for this request. The connection's navigation pointer does not select the target.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changed
    • changedInput schema / properties / project_id / description
      Previous value: -"Required immutable scope for this call. Copy the id from list_projects/open_project/project_state; the active-project pointer is never used to guess."New value: +"Explicit project identifier for this request. The connection's navigation pointer does not select the target."
  2. Added

TDQS

B3.4/5.0
Behavior3/5

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

Annotations already cover the safety profile (mutating, non-idempotent, non-destructive, closed-world), and the description adds real context: validation happens before application, strength is configurable, and there is a maximum supported grid size that can cause rejection. It does not say whether an existing LUT is replaced or stacked, what validation failures look like, or whether asset_key and url are mutually exclusive, and 'from a URL' sits in some tension with openWorldHint=false.

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?

A single sentence, front-loaded with the core action and then the qualifying details. Every clause carries information: source modes, time window, strength, and grid-size limit; nothing is restated from the title.

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

Completeness3/5

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

Given six parameters, no output schema, and only partial schema descriptions, the definition covers the intent and the main knobs, so an agent could attempt a call. It omits the mutual-exclusivity of asset_key vs url, strength defaults/bounds, and any indication of what a successful application returns or how validation errors surface.

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?

With only 50% schema coverage, the description compensates by mapping concepts to the undocumented parameters: asset_key and url become 'project asset or URL', strength becomes 'configurable strength', and start/end become the 'program-time window'. It does not supply units for strength, its valid range, or the exclusivity/defaults the schema leaves blank.

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?

States specific verbs and resource: 'Validates and applies a CUBE lookup table', plus the two source modes (project asset or URL) and the program-time window scope. An agent can tell this is LUT application, but the description never distinguishes it from color-adjacent siblings like apply_look, apply_recipe, set_color_grade, or match_grade.

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 when-to-use, when-not-to-use, or prerequisite guidance. The only implicit signal is the choice of source (asset vs URL), and nothing tells the agent when a LUT import is preferred over apply_look or set_color_grade, nor whether it must run before other grading operations.

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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