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bulk_ingest_to_clone

Ingests multiple audio files into voice-samples store, creating one voice-clone entry per file for ElevenLabs multi-sample training. Reports per-file results for partial failure visibility.

Instructions

Copy a batch of audio files into the voice-samples store, creating one voice-clone entry per file (ElevenLabs multi-sample training model). Use this after scan_folder_for_media finds the files the user wants to clone from — typical flow: scan → confirm with user → bulk_ingest_to_clone(filtered paths) → submit_voice_clone_to_cloud on each resulting cloneId. Returns per-file results so partial failures are visible. Capped at 100 paths per call (chunk larger sets). Audio extensions: webm, wav, mp3, ogg, m4a, flac. Mismatched extensions are reported as failures within the response, not as a top-level error.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
pathsYesArray of audio file paths to ingest. Each becomes one clone entry.
namePrefixNoOptional name prefix. Each clone gets "${prefix} (i/N)". Defaults to "Bulk import YYYY-MM-DD".
Behavior4/5

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

With no annotations, the description covers key behaviors: per-file results, partial failures, chunking advice, allowed extensions, and error handling. It does not disclose authorization requirements or data persistence details, but these are reasonable omissions given the tool's integration context.

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 front-loaded with the core function, followed by usage context, behavioral details, and constraints. It is well-structured without being verbose. Minor redundancy in per-file results mention could be tightened.

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?

Given the lack of output schema, the description explains that per-file results are returned. It covers the typical workflow, constraints, and error handling. It is complete enough for a batch tool with clear parameters and sensible defaults.

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 100%, so baseline is 3. The description adds value by explaining the namePrefix default format and the audio extensions for paths. It also advises chunking for large sets, which goes beyond 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 it copies audio files into a voice-samples store and creates voice-clone entries. It specifies the ElevenLabs multi-sample training model and distinguishes the batch operation from single-file alternatives by referencing the typical flow with scan_folder_for_media and submit_voice_clone_to_cloud.

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 explicitly says to use after scan_folder_for_media and outlines the flow. It highlights the cap of 100 paths, supported extensions, and error handling for mismatched extensions. It does not explicitly mention when not to use (e.g., for single files), but the implied context is clear.

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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