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generate_chirp

Create a frequency sweep (chirp) in the currently selected Audacity time range. Set waveform, start/end frequency, and amplitude to produce test tones for audio analysis or equipment calibration.

Instructions

Generate a chirp (frequency sweep). Fills the currently selected time range - call select_region first to control duration.

Args: waveform: "Sine", "Square", "Sawtooth", "Square, no alias", or "Triangle". Default: "Sine" start_freq: Starting frequency in Hz (>= 1). Default: 440 end_freq: Ending frequency in Hz (>= 1). Default: 1320 start_amp: Starting amplitude (0-1). Default: 0.8 end_amp: Ending amplitude (0-1). Default: 0.1

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
end_ampNo
end_freqNo
waveformNoSine
start_ampNo
start_freqNo

Schema Changelog

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

  1. First observedv0.1.0

TDQS

A4.3/5.0
Behavior3/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It does explain the core behavior (fills selected range, duration controlled by select_region) and parameter constraints, but it does not state whether existing audio in the selected range is overwritten or what the tool returns on success. This is a notable gap for a generation tool.

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?

The description is compact and front-loaded with the core purpose, then states the critical prerequisite, then lists parameter semantics in a clean, scannable Args block. Every sentence earns its place without padding.

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 5-parameter tool with no annotations and no output schema, the description is largely complete: it defines the action, the required selection context, all parameter meanings, and defaults. The main missing piece is explicit disclosure of side effects on existing audio, such as whether the chirp replaces the selection or is layered into it.

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?

Schema description coverage is 0%, so the description must fully compensate. It does: every parameter is explained with allowed values, ranges, and defaults, e.g., waveform options, frequency >= 1 Hz, amplitude 0-1, and all defaults. This is exactly the semantic detail the schema lacks.

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 immediately states a specific verb and resource: 'Generate a chirp (frequency sweep).' It adds the key scoping behavior 'Fills the currently selected time range,' which clearly distinguishes it from sibling tools like generate_tone, generate_noise, and generate_dtmf.

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 gives clear usage context by stating that the tool fills the currently selected time range and explicitly instructs the agent to 'call select_region first to control duration.' It does not explicitly name alternatives or exclusions, but the prerequisite is precise and actionable.

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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