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label_add_batch

Batch-add multiple audio labels in one call, validated upfront so invalid entries fail the whole batch instead of leaving incomplete data.

Instructions

Add many labels at once — a whole marker list in one call.

Each item is {"start": seconds, "end": seconds (optional, defaults to start for a point label), "text": string (optional)}. Every item is validated before anything is sent to Audacity, so a bad item fails the whole call rather than leaving a half-written list behind.

Args: labels: List of {"start", "end", "text"} label definitions

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
labelsYes
Behavior4/5

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

No annotations are provided, so the description carries full burden. It discloses the batch validation behavior (all validated before sending to Audacity) and failure semantics (bad item fails whole call). This is genuinely useful behavioral context beyond the schema.

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?

Front-loaded purpose ('Add many labels at once'), then a concise format spec, then the important atomicity caveat. No wasted sentences. The Args block is redundant with the schema but the item structure detail justifies its inclusion given the schema is empty.

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 batch-add tool with only one parameter and no output schema, the description covers the input format, defaults, validation behavior, and failure semantics. Slightly more could be said about return value or what happens on partial failure, but the atomicity disclosure largely covers that. Well-suited for the tool's 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 coverage is 0% and the item schema is just 'additionalProperties: true' with no property definitions, so the schema provides no meaning. The description compensates by documenting the structure {start, end optional, text optional} with defaults, which is valuable. However, it doesn't describe units (seconds confirmed), boundaries, or validation rules in detail.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

Clear verb+resource ('Add many labels at once') with a specific behavior (batch add of a whole marker list). Distinguishes from siblings like label_add by emphasizing the batch nature, though it doesn't explicitly name a sibling alternative.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies batch use for adding multiple labels at once, and mentions the atomicity behavior (whole call fails vs half-written list). However, it doesn't explicitly state when to prefer this over label_add (single) or label_add_at, nor any exclusions.

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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