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mcp_opendaw_detect_bpm

Read-only

Analyze a WAV file to detect its tempo (BPM) using onset detection and autocorrelation, enabling beat alignment in music production.

Instructions

Detect BPM (tempo) of an exported WAV file using onset detection + autocorrelation.

Pure Python implementation (no external dependencies):

  1. Parse WAV → mono mixdown

  2. Energy envelope (1024-sample windows)

  3. Onset detection (energy spikes above local average)

  4. Autocorrelation of onset train → dominant periodicity → BPM

Essential for Suno integration: Suno generates at its own BPM, but the openDAW project needs matching tempo. Detect → set_bpm for correct beat alignment when placing stems.

filename: Name of the WAV file in the exports directory (without path), or absolute path to any WAV file.

Returns: bpm (60-200), confidence (0-1), onset_count, duration_seconds.

Examples:

After importing a Suno track

result = detect_bpm("suno_track.wav")

→ {bpm: 128.0, confidence: 0.85, onset_count: 240, ...}

Then set project BPM

set_bpm(128)

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
filenameYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior5/5

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

Beyond the readOnlyHint annotation, the description details the algorithm (onset detection + autocorrelation), pure Python implementation, return fields with ranges, and the use case. No contradictions with annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is well-structured with a clear purpose, algorithm steps, use case, parameter, return fields, and example. It is slightly verbose with algorithm details but remains front-loaded and easy to parse.

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?

Given the tool's simplicity (one parameter, no nested objects), the description covers all essential aspects: purpose, usage, parameter, returns, and example. The presence of an output schema means return values need not be detailed further.

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

Parameters5/5

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

With 0% schema description coverage, the description thoroughly explains the 'filename' parameter: it can be a name in the exports directory or an absolute path, and includes an example. This fully compensates for missing schema descriptions.

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 clearly states 'Detect BPM (tempo) of an exported WAV file' with a specific verb and resource. It distinguishes from siblings like mcp_opendaw_set_bpm by outlining the detect→set workflow for Suno integration.

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?

The description explains when to use it (for Suno tracks before setting BPM) and provides a usage example. However, it does not explicitly list alternative tools or scenarios where this tool should be avoided.

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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