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mcp-agent-chatterbox

mcp-agent-chatterbox

by ChristofMilius
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
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list_voices

Lists available voice-cloning reference clips in the voices directory with names, file details, duration, sample rate, channels, and compatible models. Use it to pick a compatible clip.

Instructions

List the reference clips available for voice cloning — the files in the voices directory. Returns each clip's name (what you pass as voice=), filename, size, duration, sample rate and channel count, plus which models can use it. The multilingual and original models require one of these; turbo does not.

For good cloning use as much clean single-speaker audio as you have, not a short excerpt — clipping a good recording down measurably adds fine clicks and crackle. A mastered or compressed source is fine; do not normalise it. Sample rate and channel count need not match anything. Clips too short to be safe carry a quality_note.

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "list_voices"
  }
}

Input Schema

NameRequiredDescriptionDefault

No arguments

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4.3/5.0
Behavior4/5

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

With no annotations, the description carries the behavioral burden and does well: it describes the returned fields (name, filename, size, duration, sample rate, channel count, model compatibility) and notes that too-short clips carry a quality_note. It does not explicitly state that the operation is read-only or mention any auth/rate-limit behavior, but those are largely implied by 'List' and not critical here.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The first paragraph is front-loaded and efficient. The second paragraph, while relevant to voice cloning, contains detailed audio-quality advice ('clipping... adds fine clicks and crackle', 'do not normalise it') that is not necessary for selecting or invoking list_voices and adds tangential length.

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?

For a zero-parameter listing tool with an output schema, the description is complete enough: it explains what is returned, notes the quality_note field, and clarifies which models depend on these clips. No critical information for correct invocation is missing.

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?

The tool takes zero parameters, so the baseline is 4. The description mentions voice= only as context for other tools, not as a parameter of this one, and the schema is empty.

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 uses a specific verb and resource: 'List the reference clips available for voice cloning — the files in the voices directory.' It is immediately distinguishable from siblings like speak, stop_speech, tts_status, and tts_unload, all of which perform different actions.

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?

It provides clear context for when the listed clips are needed: 'The multilingual and original models require one of these; turbo does not.' There are no alternative listing tools to compare against, but the description does not explicitly state exclusions or prerequisites beyond model compatibility.

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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