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analyze_track_loudness

Measure track loudness metrics like LUFS and true peak over a specified time range without altering your REAPER project.

Instructions

Measure the loudness of a single track over a time range using a non-destructive dry-run render (action 42439). No items, tracks, or files are created — project state is completely unchanged after the call. Returns:

  • lufs_i: integrated loudness in LUFS

  • lufs_s_max: maximum short-term loudness in LUFS

  • lufs_m_max: maximum momentary loudness in LUFS

  • true_peak_db: true peak in dBTP

  • raw_stats: raw key=value string from REAPER for any additional fields

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
track_indexYes
start_timeYes
end_timeYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Implementation Reference

  • MCP tool registration and handler for `analyze_track_loudness`.
    def analyze_track_loudness(
        track_index: int,
        start_time: float,
        end_time: float,
    ) -> dict[str, Any]:
        """
        Measure the loudness of a single track over a time range using a non-destructive
        dry-run render (action 42439). No items, tracks, or files are created —
        project state is completely unchanged after the call.
        Returns:
        - lufs_i: integrated loudness in LUFS
        - lufs_s_max: maximum short-term loudness in LUFS
        - lufs_m_max: maximum momentary loudness in LUFS
        - true_peak_db: true peak in dBTP
        - raw_stats: raw key=value string from REAPER for any additional fields
        """
        try:
            return _wrap(
                adapter.analyze_track_loudness(
                    track_index=track_index,
                    start_time=start_time,
                    end_time=end_time,
                )
            )
  • Adapter method that dispatches `analyze_track_loudness` request to the bridge client.
    def analyze_track_loudness(
        self,
        track_index: int,
        start_time: float,
        end_time: float,
    ) -> dict[str, Any]:
        return self._client.call(
            "analyze_track_loudness",
            track_index=track_index,
            start_time=start_time,
            end_time=end_time,
        )
Behavior5/5

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

No annotations provided, yet description excellently discloses behavioral traits: explicitly states 'non-destructive dry-run render', clarifies 'No items, tracks, or files are created', and guarantees 'project state is completely unchanged'. Also documents complete return value structure (LUFS values, true peak, raw stats).

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?

Perfectly structured with zero waste. First sentence establishes purpose and mechanism, second sentence provides safety guarantees, followed by a clear structured list of return values. Every sentence earns its place and critical safety info is front-loaded.

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

Completeness5/5

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

With rich output structure implied by the return documentation and zero schema coverage, the description successfully covers: purpose, operational method (dry-run), safety guarantees, and complete output format. No gaps remain for a tool of this complexity.

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. Description partially compensates by mentioning 'single track' (mapping to track_index) and 'time range' (mapping to start/end_time), providing semantic meaning. However, lacks critical details like time units (seconds?) or track indexing scheme (0-based vs 1-based) that the schema should have provided.

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 specific action ('Measure the loudness') on specific resource ('single track over a time range'). The phrase 'single track' effectively distinguishes this from sibling tool 'analyze_master_loudness', clarifying this is per-track analysis rather than master bus analysis.

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 non-destructive 'dry-run' nature which implies safe usage without side effects. The 'single track' qualifier implicitly guides selection against the master loudness alternative. Lacks explicit 'when not to use' exclusions but provides clear operational context.

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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