Daisys MCP Server

Overview

The Daisys MCP server provides a bridge between AI assistants and the Daisys voice‑and‑audio platform. It solves a common pain point for developers: connecting language models to real‑time audio services without having to write custom authentication, streaming logic, or file‑management code. By exposing Daisys’s capabilities through the Model Context Protocol, developers can invoke speech‑to‑text, text‑to‑speech, and other media utilities directly from their preferred MCP client—whether that’s Claude Desktop, Cursor, or VS Code.

At its core, the server authenticates with Daisys using simple environment variables (email and password) and then exposes a set of tools that handle audio capture, transcription, and synthesis. The server stores audio files in a user‑defined directory, allowing developers to manage local assets while still leveraging Daisys’s cloud processing. This design keeps the AI workflow lightweight: the assistant sends a prompt to the server, receives a processed audio file or transcription, and continues the conversation without leaving the client interface.

Key features include:

• Seamless authentication via environment variables, eliminating hard‑coded credentials.
• Automatic audio storage with a configurable base path, making it easy to archive or reuse recordings.
• Cross‑platform support (macOS and Linux) with clear dependency guidance for PortAudio, ensuring the server runs on most developer machines.
• Integration-ready configuration that works out of the box with popular MCP clients, so developers can focus on building experiences rather than plumbing.

Typical use cases span interactive voice assistants, real‑time transcription tools for meetings, and multimodal applications that combine text prompts with spoken feedback. For example, a developer building a conversational UI can let the assistant speak responses through Daisys’s TTS engine, while also capturing user speech via STT for dynamic dialogue. In educational settings, the server can power language learning apps that require spoken input and pronunciation evaluation.

What sets Daisys MCP apart is its minimalistic yet powerful integration layer. It abstracts away the complexities of audio streaming and authentication, allowing developers to treat Daisys as a first‑class tool within the MCP ecosystem. This enables rapid prototyping of voice‑enabled AI workflows, reduces boilerplate code, and ensures consistent behavior across different client platforms.