Chatterbox TTS
This MCP server provides text-to-speech functionality using either the high-quality Chatterbox TTS neural model or macOS's built-in 'say' command, automatically generating speech from text and playing it back with configurable expressiveness controls. Built by digitarald using Python and FastMCP, it features automatic model loading with progress notifications, persistent audio file storage with configurable TTL cleanup, dual TTS engine support for flexibility between quality and speed, and specialized prompts for daily haikus and code roasts. The implementation handles device optimization (MPS/CUDA/CPU), provides real-time progress updates during speech generation, and includes comprehensive audio resource management with embedded playback, making it valuable for developers who want AI assistants to provide spoken feedback, code reviews, or daily engagement through voice interaction.
Composite of vulnerability cleanliness, spec conformance, provenance, stability, and usage signals — scanned and weighted by Cognium. Human and agent signals are tracked separately. Last scanned 2026-09-28.
Scan details: Circle-IR · 2026-09-28 · Appeal
View full trust & usage report →Metadata
- Version
- 1.0.0
- Skill type
- atomic
- Execution layer
- mcp-remote
- Category
- media
- Source
- PulseMCP
- Repository
- github.com/digitarald/chatterbox-mcp
- Author type
- human
- Last scanned
- 2026-09-28
- Updated
- 2026-09-28
Use via MCP
Resolve Chatterbox TTS from your agent
Streamable HTTP transport at https://api.skillsregistry.net/mcp. No auth for read tools. Discovery: .well-known/mcp.json.
One command in your shell — Claude Code wires it up and verifies the connection. Run /mcp in any session to confirm.
claude mcp add --transport http --scope user skillsregistry https://api.skillsregistry.net/mcp--scope user for --scope project to commit it to .mcp.json.