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analyze_find_clipping

Detect audio clipping in selected regions and label clipped areas to identify distortion for correction.

Instructions

Find clipping in the selected audio and create labels at clipped regions.

Args: duty_cycle_start: Min number of consecutive clipped samples to detect (1-1000, default 3) duty_cycle_end: Min number of consecutive non-clipped samples to end a region (1-1000, default 3)

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
duty_cycle_startNo
duty_cycle_endNo
Behavior4/5

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

With no annotations provided, the description carries the full burden. It successfully discloses the key behavioral trait that the tool creates labels as a side effect and operates on 'selected audio' (scoping constraint). It does not explicitly state that the operation is non-destructive/read-only, though this is reasonably implied by the 'analyze' prefix and label-creation description.

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 efficiently structured with the primary purpose front-loaded in the first sentence, followed by a clear Args section. Every sentence earns its place, and the parameter documentation is concise yet complete.

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 low complexity (2 optional parameters, no output schema) and lack of annotations, the description is appropriately complete. It explains what the tool does, its scope, its side effects (label creation), and fully documents all parameters.

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%, requiring the description to fully compensate. The Args section excellently documents both parameters with semantic meaning ('Min number of consecutive clipped samples'), valid ranges (1-1000), and default values (3), providing complete information not present in the 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?

The description clearly states the specific action ('Find clipping') and resource ('selected audio'), plus the distinct outcome ('create labels at clipped regions'). This effectively distinguishes it from sibling effect_clip_fix (which repairs clipping) and other analyze_* tools that perform different analyses.

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 provides clear context about the tool's function (detection and labeling) without explicitly naming alternatives. It implies usage through the specific action described, though it does not explicitly state when to prefer this over effect_clip_fix or other analysis tools.

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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