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normalize_track

Adjust track volume to achieve target loudness by measuring LUFS and applying gain, supporting streaming, broadcast, and podcast standards.

Instructions

Normalize a track to a target integrated loudness by measuring its current LUFS via a non-destructive dry-run render, then adjusting the track fader.

  • target_lufs: desired integrated loudness in LUFS (default -14.0, streaming standard). Use -23.0 for EBU R128 broadcast, -16.0 for podcast. Returns measured_lufs_i, gain_applied_db, and old/new fader volumes. The change is registered in REAPER's undo history.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
track_indexYes
start_timeYes
end_timeYes
target_lufsNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Implementation Reference

  • The MCP tool registration and entry point for 'normalize_track'. It wraps the adapter call.
    @mcp.tool()
    def normalize_track(
        track_index: int,
        start_time: float,
        end_time: float,
        target_lufs: float = -14.0,
    ) -> dict[str, Any]:
        """
        Normalize a track to a target integrated loudness by measuring its current
        LUFS via a non-destructive dry-run render, then adjusting the track fader.
        - target_lufs: desired integrated loudness in LUFS (default -14.0, streaming standard).
          Use -23.0 for EBU R128 broadcast, -16.0 for podcast.
        Returns measured_lufs_i, gain_applied_db, and old/new fader volumes.
        The change is registered in REAPER's undo history.
        """
        try:
            return _wrap(
                adapter.normalize_track(
                    track_index=track_index,
                    start_time=start_time,
                    end_time=end_time,
  • The implementation of the adapter method 'normalize_track', which invokes the remote client call.
    def normalize_track(
        self,
        track_index: int,
        start_time: float,
        end_time: float,
        target_lufs: float = -14.0,
    ) -> dict[str, Any]:
        return self._client.call(
            "normalize_track",
            track_index=track_index,
            start_time=start_time,
            end_time=end_time,
            target_lufs=target_lufs,
        )
Behavior5/5

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

No annotations exist, so description carries full burden. Excellently discloses: internal mechanism (dry-run render), specific mutation target (track fader), return value structure (measured_lufs_i, gain_applied_db, old/new volumes), and critical side effect (registered in REAPER's undo history). No contradictions.

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?

Four sentences with zero waste: purpose/mechanism front-loaded, followed by parameter guidance, return values, and side effects. Every sentence earns its place with domain-specific precision (LUFS, EBU R128).

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?

Excellent behavioral and domain coverage given the complexity of loudness normalization. The 0% schema coverage and missing parameter semantics for time ranges and track indexing prevent a 5, but output schema existence and rich mechanism description provide sufficient context for invocation.

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 has 0% description coverage, requiring description to compensate. It provides exemplary semantic detail for target_lufs (units, default, broadcast standards) but completely omits track_index (0-indexed?), start_time, and end_time (units? project time vs. item time?), leaving 3 of 4 parameters undocumented.

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 provides a specific verb (normalize), resource (track), and detailed mechanism (measuring LUFS via non-destructive dry-run render, adjusting track fader) that clearly distinguishes it from sibling analyze_track_loudness which presumably only measures without adjusting.

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?

Provides excellent domain-specific guidance for target_lufs values (-14.0 streaming, -23.0 EBU R128, -16.0 podcast). Lacks explicit 'when not to use' or direct sibling comparisons, but the mechanism description implies this is for final output leveling vs. simple analysis.

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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