Kokoro Text to Speech (TTS) MCP Server

Overview

The Kokoro Text to Speech (TTS) MCP Server turns plain text into high‑quality audio files, delivering .mp3 outputs that can be stored locally or uploaded to Amazon S3. Designed for integration with AI assistants such as Claude, it fills the gap between natural language generation and audible response delivery. Developers can embed voice synthesis directly into conversational agents, enabling dynamic audio playback without leaving the MCP ecosystem.

What Problem It Solves

In many AI‑powered applications, text output is insufficient—users expect spoken responses in chatbots, accessibility tools, or interactive voice interfaces. Existing TTS solutions often require separate services, complex API keys, or proprietary licensing. Kokoro TTS consolidates the entire workflow into a single MCP server: text input, neural synthesis via an ONNX model, and optional cloud storage—all configurable through environment variables. This eliminates the need for third‑party TTS APIs, reduces latency by running locally, and gives developers fine control over voice selection, speed, and language.

Core Features & Value

• ONNX‑powered synthesis: Uses the pre‑trained Kokoro model () with a voices binary (), ensuring fast inference and low resource usage on modern CPUs or GPUs.
• Flexible voice & speed control: Environment variables (, ) and client flags allow on‑the‑fly customization, supporting a wide range of accents and speaking rates.
• Automatic MP3 conversion: A lightweight ffmpeg wrapper converts raw .wav output to compressed .mp3, suitable for web delivery and storage.
• Seamless S3 integration: When is true, generated files are uploaded to a specified bucket and folder. Post‑upload cleanup () keeps local storage tidy.
• Lifecycle management: triggers automated deletion of old files, preventing storage bloat without manual intervention.
• Developer‑friendly configuration: All parameters are exposed via environment variables or command‑line flags, allowing quick adjustments in CI/CD pipelines or local development.

Use Cases & Real‑World Scenarios

• Conversational AI: Convert chatbot replies into spoken audio for mobile or web clients, enhancing user engagement.
• Accessibility: Generate narration for visually impaired users on the fly, with customizable voice and speed.
• Educational tools: Produce pronunciation guides or language learning audio snippets directly from text prompts.
• Content creation pipelines: Automate voice‑over generation for videos, podcasts, or e‑learning modules, with automatic S3 uploads for distribution.
• IoT and embedded devices: Run the server on edge hardware to provide local TTS without cloud latency, while optionally syncing results to the cloud for analytics.

Integration with AI Workflows

Developers can invoke the server through MCP’s resource interface or via the bundled . The client accepts text, voice, speed, and S3 flags, sending a structured request that the server interprets and executes. The response includes the path to the generated MP3 (local or S3 URL), enabling downstream services—such as media players, messaging platforms, or storage backends—to consume the audio immediately. Because all operations are stateless and driven by environment variables, the server scales horizontally in containerized environments or as a lightweight microservice alongside other MCP components.

Standout Advantages

• Zero external dependencies beyond ffmpeg and the ONNX model, keeping the runtime footprint minimal.
• Full control over data residency: choose local storage or S3, and configure retention policies.
• Open‑source model integration: no licensing fees or usage limits, unlike commercial TTS APIs.
• Built for MCP ecosystems: seamless discovery and invocation via standard protocols, making it a drop‑in replacement in existing assistant architectures.

In summary, the Kokoro TTS MCP Server empowers developers to add natural‑sounding voice output to AI assistants with minimal overhead, robust configuration options, and optional cloud integration—all while staying within the MCP framework.