"Turbo" matching MCP tools:

• scan_project

  DepScope

  Audit a project's dependencies in one shot. Returns a single-sentence `verdict` (e.g. "DO NOT INSTALL — 1 hallucinated: fastapi-turbo") that an agent can paste into its reply, plus per-package health/vulns/recommendation. Detects hallucinated packages, deprecated, typosquats, critical vulnerabilities. Accepts EITHER {ecosystem, packages:[name@ver, …]} (up to 100, returns JSON) OR {packages:[{ecosystem, package}, …]} (up to 50, mixed ecosystems, returns text brief). USE WHEN: user pastes package.json/requirements.txt/Cargo.toml; agent generated install command; 'is my stack OK'. RETURNS: JSON with `verdict`, `project_risk`, `summary.hallucinated_packages`, `summary.deprecated_packages`, per-package health.

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• clone_voice

  Sats4AI - Bitcoin-Powered AI Tools

  Clone any voice from a single audio sample. Returns a reusable voice_id for text_to_speech — speak in the cloned voice indefinitely. High-fidelity reproduction capturing tone, cadence, and accent. Turbo (faster) or HD (higher quality) modes. 7,500 sats per clone. Pay per request with Bitcoin Lightning — no API key or signup needed. Requires create_payment with toolName='clone_voice'.

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• check_whisper_service

  Speech AI - Pronunciation, STT & TTS

  Check if the Whisper STT Pro service is healthy and ready. Returns: dict with keys: - status (str): 'healthy' or error state - modelLoaded (bool): Whether the Whisper model is loaded - diarizeLoaded (bool): Whether the diarization pipeline is loaded - version (str): API version - modelName (str): Whisper model name (e.g. 'large-v3-turbo')

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• generate_fashion_video

  Smasher Studio — AI Fashion Design

  Submit an async video generation job from an existing image. Returns a job_id immediately — call check_video_status to poll for completion. Uses Kling 3.0 (primary, 1080p, native audio) with Seedance and Runway Gen-4 Turbo fallbacks. Costs 100-300 credits based on duration.

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• transcribe_audio_pro

  Speech AI - Pronunciation, STT & TTS

  Transcribe audio with Whisper Large V3 Turbo — multilingual STT. Supports 99 languages with automatic language detection, word-level timestamps, per-word confidence scores, and optional speaker diarization (identifies who spoke each word). Best-in-class WER (~2%). Args: audio_base64: Base64-encoded audio (WAV, MP3, OGG, FLAC, WebM). language: Language code. Auto-detected if omitted. Supports 99 languages. diarize: Enable speaker diarization (default: false). When true, each word includes a speaker label (e.g. SPEAKER_00, SPEAKER_01). Returns: dict with keys: - text (str): Full decoded transcript - words (list): Per-word results with timestamps, each containing: - word (str), start (float), end (float), confidence (float 0-1) - speaker (str|null): Speaker label when diarize=true - speakers (dict|null): Speaker info with count and labels - audioDurationMs (int): Audio duration in milliseconds - metadata (dict): Processing time, language, languageProbability - audioQuality (dict): Audio metrics (SNR, peak/RMS dB, etc.)

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• list_audio_catalog

  Tenzro Ledger MCP

  Browse the curated ONNX ASR catalog: Moonshine v2 (tiny/base, MIT, on-device), Distil-Whisper (small.en/medium.en/large-v3, MIT), Whisper Large-v3-turbo (MIT, flagship), Parakeet-TDT-0.6B-v3 (CC-BY-4.0, 25 European langs), Canary-1B-Flash (CC-BY-4.0, multilingual). The catalog is stable; the ORT-backed transcribers ship in the next wave (today the runtime returns ProviderNotAvailable).

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• load_audio_model

  Tenzro Ledger MCP

  Load an ASR ONNX model (Moonshine, Distil-Whisper, Whisper-v3-turbo, Parakeet-TDT-v3, Canary-1B-Flash). Either pass `catalog_id` to inherit `family` / `max_audio_seconds` / `whisper_variant` from the catalog, or set them explicitly. The transcriber lands in a follow-up wave — today, `transcribe` returns ProviderNotAvailable; loading is wired for end-to-end testing once the transcriber arrives.

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• gr1_z_image_turbo_generate

  Hugging Face

  Generate an image using the Z-Image model based on the provided prompt and settings. This function is triggered when the user clicks the "Generate" button. It processes the input prompt (optionally enhancing it), configures generation parameters, and produces an image using the Z-Image diffusion transformer pipeline. Returns: tuple: (gallery_images, seed_str, seed_int), - seed_str: String representation of the seed used for generation, - seed_int: Integer representation of the seed used for generation (from mcp-tools/Z-Image-Turbo)

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