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audio_detect_tempo

Detect the tempo of an audio file in beats per minute, using onset autocorrelation within a configurable BPM range. Outputs a confidence-scored BPM for use in music production workflows.

Instructions

Detect the BPM of an audio file via onset autocorrelation. bpm_range bounds the search (default 60..180). Returns a single BPM with confidence 0..1. Works well on EDM/pop loops with a kick on every beat; less reliable on free-tempo or sparse music.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
bpm_maxNo
bpm_minNo
file_pathYes

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observedv0.1.0

TDQS

A4.4/5.0
Behavior4/5

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

No annotations are present, so the description carries the behavioral burden. It discloses the output format (single BPM with confidence 0..1) and the reliability across music styles, which is valuable. It doesn't state side effects or permission needs, but for a read-only detection operation this is minor.

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 sentences, zero waste. The purpose, algorithm, parameters, output, and limitations are all covered efficiently, with the most critical information (what it does) 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?

For a simple detection tool, the description covers the essential aspects: what it returns, how parameters behave, and when it works well. It lacks explicit error-handling or file-format details, but these are not critical for a straightforward BPM detection task. The output schema is absent, so the return description is essential and provided.

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?

Schema coverage is 0%, so the description must explain parameters. It clarifies bpm_min and bpm_max as a search range with defaults, and identifies file_path as the audio file. While it doesn't specify path formatting or error cases, it adds meaning beyond the bare schema.

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 action (detect BPM) on a resource (audio file) and the method (onset autocorrelation). This clearly distinguishes it from siblings like audio_detect_key (key detection) and audio_compare (audio comparison), leaving no ambiguity about its function.

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 explicit conditions for good performance (EDM/pop with steady kicks) and limitations (free-tempo or sparse music), helping the agent decide when this tool is appropriate. It doesn't explicitly name alternative tools for other scenarios, but the context is sufficient for routing.

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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