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ChristofMilius

mcp-agent-chatterbox

speak

Generate speech audio from text with Chatterbox on local GPU, save as WAV, and play through system audio.

Instructions

Speak text aloud with Chatterbox on the local GPU, save it as a WAV, and by default play it through the system audio device.

model: "turbo" (default, 350M, English, no reference clip needed, supports inline [laugh]/[chuckle] tags), "multilingual" (500M, 23 languages, requires a reference clip) or "original" (500M, English, requires a reference clip). voice: name of a reference clip in the voices directory (its filename without extension). Required for multilingual/original. reference_clip: path to a wav/mp3/flac clip, as an alternative to voice=. Supply as much clean continuous speech as you have. Short references measurably increase fine clicks and crackle: a 40 s reference beat an 11.8 s excerpt of the same recording by a wide margin, several times the model's own run-to-run variance. Under ~5 s is genuinely too little. Do not normalise or limit the clip -- that shifts output level and adds artefacts. A mastered or compressed recording is fine. Sample rate and channel count need not match anything. language: ISO 639-1 code, multilingual only (e.g. "de", "en", "fr"). t3_model: "v2" (default) or "v3" — multilingual checkpoint. temperature: sampling temperature (0.05-5.0). Honored by all models. top_p: nucleus-sampling cutoff (0.0-1.0). Honored by all models. top_k: top-k sampling size (0-1000). Honored by turbo only — the 500M models have no such parameter. repetition_penalty: penalise repeated tokens (1.0-2.0). Honored by all models. norm_loudness: normalize output to -27 LUFS. Honored by turbo only — the 500M models have no such parameter. exaggeration/cfg_weight: style controls (0.0-2.0 / 0.0-1.0). Honored ONLY by the 500M models (multilingual/original); turbo ignores both, so setting them with model="turbo" is dropped with a warning, not an error. cfg_weight>0 doubles the text tokens for CFG guidance. seed: reseed torch (CPU + CUDA) so re-renders are reproducible within the resident session; 0 (the upstream convention) or unset keeps random sampling. Byte-identical output across a server restart is not guaranteed — Chatterbox is nondeterministic across CUDA kernel choices. Left unset, every knob keeps the model's own tuned default (e.g. turbo runs cfg_weight 0.0 with top_k 1000; the 500M models run cfg_weight 0.5). play_audio: set false to only write the file. Defaults to the server setting (on). wait: block until playback finishes instead of returning immediately. filename: output basename; a safe name is generated when omitted.

The first call downloads the model from Hugging Face (hundreds of MB to a few GB) and can take a while; later calls reuse the cached weights. Returns JSON with the output filename, duration, sample rate, model, device and whether it played.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
seedNo
textYes
waitNo
modelNo
top_kNo
top_pNo
voiceNo
filenameNo
languageNo
t3_modelNo
cfg_weightNo
play_audioNo
temperatureNo
exaggerationNo
norm_loudnessNo
reference_clipNo
repetition_penaltyNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4.3/5.0
Behavior5/5

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

With zero annotations, the description carries the full burden and does so well: it discloses the first-call model download (hundreds of MB to a few GB, slow) and subsequent cache reuse, the file-write and audio-play side effects, that play_audio defaults to a server setting, that wait blocks, and that a safe filename is auto-generated. It also surfaces edge behavior an agent could not guess — ignored parameters produce a warning not an error, cfg_weight doubles token count, and byte-identical output is not guaranteed across a restart due to CUDA nondeterminism.

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 one-line purpose is front-loaded, followed by a consistent per-parameter reference block, which is an efficient structure for 17 parameters. It runs long and the reference-clip anecdote (40 s vs 11.8 s excerpt) is more detailed than strictly necessary, but overall each entry earns roughly its place given the parameter count.

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 17-parameter, annotation-free synthesis tool, the description is complete: it covers defaults, model/param interactions, side effects, download latency, and even summarizes the JSON return. With an output schema present it correctly does not need to detail return structure, so nothing an agent needs to invoke this correctly is missing.

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 supply all parameter meaning, and it does: every knob (model, voice, reference_clip, language, t3_model, temperature, top_p, top_k, repetition_penalty, norm_loudness, exaggeration/cfg_weight, seed, play_audio, wait, filename) gets a definition, valid range, and the model-compatibility rule (turbo-only vs 500M-only params). It even adds non-obvious practical guidance on reference-clip length and loudness normalization that the schema could never convey.

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?

The opening sentence states a specific verb and resource: synthesize text with a named engine, write a WAV, and by default play it. It clearly reads as the 'do the speech synthesis' action versus the lifecycle siblings (tts_status, tts_unload, stop_speech, list_voices). However, it never names or contrasts those siblings explicitly, so distinctness is inferred rather than stated.

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?

It explains the conditions that select a model (turbo needs no reference clip; multilingual/original require one) and the meaning of defaults, which is useful context. But there is no explicit when-to-use/when-not guidance and no routing to siblings — e.g. it never says to use list_voices to discover a valid voice name or stop_speech to interrupt playback.

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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