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remove_sequence_track

Destructive

Delete a track from a Level Sequence by display name or exact class name, optionally filtered by track class. Use to remove unwanted tracks from a sequence.

Instructions

Delete any track from a sequence by display name OR exact class name (optionally filtered by track_class).

Returns: { sequence_path: string, track_name: string, track_class: string, sections_removed: int, removed: bool, saved: bool, save_error?: string }

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
track_nameYesDisplay name OR class short name of the track to remove.
track_classNoOptional exact class filter (e.g. "MovieSceneFadeTrack") to disambiguate.
sequence_nameNoAlias for sequence_path.
sequence_pathNoAsset path of the Level Sequence.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changedv1.2.2
    • changedInput schema / required
      Previous value: -[
      -  "sequence_path",
      -  "sequence_name",
      -  "track_name"
      -]New value: +[
      +  "track_name"
      +]
  2. First observedv1.1.0

TDQS

A4/5.0
Behavior4/5

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

Annotations already declare destructiveHint=true, readOnly=false, and non-idempotent, so safety is covered. The description adds real behavioral value beyond that: the return payload reveals that removing a track also removes its sections (sections_removed) and that the asset is persisted (saved, save_error?). It stops short of stating permission or history/undo implications.

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?

Two tight sentences: the action and its scoping rule come first, the return shape second. No filler, and the most decision-relevant information is front-loaded.

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, but the description inlines the return shape, so an agent lacks nothing about results. For a destructive mutation it is nearly complete; it could go one step further by noting the sequence must be loaded/existing or that the change is saved immediately.

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 coverage is 100%, so the schema documents all four parameters including the sequence_path/sequence_name alias. The description's 'display name OR exact class name ... optionally filtered by track_class' largely restates the schema, adding only mild emphasis on disambiguation, so baseline 3 applies.

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 specific verb and resource ('Delete any track from a sequence') and adds the disambiguation rule ('by display name OR exact class name'). An agent can identify it as the removal counterpart to the many add_*_track siblings without opening the schema.

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

Usage Guidelines3/5

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

The description implies the use case (remove a track you can name or classify) but gives no explicit when-to-use guidance, no prerequisites, and does not name an alternative such as get_sequence_tracks for locating a track first. Adequate but leaves usage to inference.

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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