Speech Synthesis MCP for AI. Generate broadcast-quality voiceovers on demand.
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Volcengine Speech Synthesis handles high-fidelity, multi-lingual text-to-speech conversion. Use this MCP to generate natural narration, including signature TikTok voice styles, from simple text or complex markup languages like SSML.
It’s built for content creators and developers needing professional audio output across English, Chinese, Japanese, and more.
What your AI can do
Get audio formats
Lists the available output formats for the generated audio (like MP3 or WAV).
List voices
Retrieves every available TTS voice model to help you select the right sound for your project.
Synthesize long text
Generates audio from texts that are too long for standard synthesis calls, like full articles or reports.
Convert any block of text into natural, spoken audio using general voice styles.
Train a custom voice model from your own high-quality audio recordings to give the AI a personalized sound.
Convert entire articles or long manuals into speech without hitting character limits.
Use markup language to dictate precise timing, pauses, and emphasis in the generated audio.
List all available voice models—including specialized styles—before beginning any synthesis job.
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Volcengine Speech Synthesis: 7 Tools
Use these seven specific functions to manage everything from training a new voice model to synthesizing massive documents with precise audio controls.
Make your AI actually useful.
Add this MCP to Claude, Cursor, or Windsurf and your AI stops guessing. It gets real tools to look things up, take action, and handle the stuff you keep doing by hand.
Start using Volcengine Speech Synthesis on VinkiusGet Audio Formats
Lists the available output formats for the generated audio (like MP3 or WAV).
List Voices
Retrieves every available TTS voice model to help you select the right sound for...
Synthesize Long Text
Generates audio from texts that are too long for standard synthesis calls, like full...
Synthesize Ssml
Uses specialized tags to control the exact timing, pauses, and emotional delivery of...
Synthesize Speech
Converts text into speech using various voice styles and supports multiple languages...
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Claude AI
Open Claude Settings
Go to claude.ai, click your profile icon, then navigate to Customize → Connectors.
Add Custom Connector
Click the "+" button and select Add custom connector. Paste your Vinkius endpoint URL:
https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp
Replace [YOUR_TOKEN_HERE] with your token
from cloud.vinkius.com. For OAuth-protected servers, expand
Advanced settings to add credentials.
Start a conversation
Open a new chat. The Speech Synthesis integration is available immediately — no restart needed.
Choose How to Get Started
Build a custom MCP for your own tools, or connect a ready-made integration from our catalog.
Build Your Own
Turn any API into an MCP. Import a spec, define Agent Skills, or deploy with MCPFusion.
- Import from OpenAPI, Swagger, or YAML specs
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Make Your AI Do More
Start with Volcengine Speech Synthesis, then connect any of our 5,100+ other servers whenever your AI needs more. One click, no limits.
- Use this MCP plus 5,100+ others, all in one place
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- Works with Claude, ChatGPT, Cursor, and more
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Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Volcengine Speech. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.
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Works with Claude, ChatGPT, Cursor, and more
The Model Context Protocol standardizes how applications expose capabilities to LLMs. Instead of operating in isolation, your AI gains direct access to external platforms, live data, and real-world actions through secure, standardized connections.
This connection provides 5 powerful capabilities that interface natively with Claude, ChatGPT, Cursor, and other compatible AI platforms. No middleware. No custom integration required.
The headache of manual voiceover production today
Right now, turning a written document into professional audio means clicking through dozens of tabs. You copy text from your draft, paste it into an external TTS site, select a voice, and download the MP3. If you need to change the pacing or add emphasis, you have to go back and manually edit the source text, repeat the process, and re-download.
With this MCP, you simply send the core content once. The platform handles all the complex synthesis steps—from choosing the right voice model to managing multi-language requirements—and returns structured audio ready for your project.
Synthesize Speech with `synthesize_speech`
You ditch the manual process of segmenting text. You don't need to worry about whether the voice supports Chinese, English, or Japanese; you just specify the desired parameters and language. The agent handles the rest.
It's immediate control. You move from needing an expensive recording session to running a single command that generates perfect audio output.
What your AI can actually do with this
This connector lets you take any written text and turn it into broadcast-quality audio. You can generate speech using ByteDance's advanced voice models—the ones behind TikTok's viral effects—for everything from quick social media clips to entire audiobooks. It supports multi-language synthesis across English, Chinese, Japanese, and more, letting you create global content without ever touching a recording studio.
Need precise timing? You can use SSML tags to dictate exactly where the speaker pauses or when they put emphasis. For massive documents, there’s a dedicated process for synthesizing long text that standard tools choke on. Because this MCP deals with sensitive keys and high-volume audio generation, your credentials pass through Vinkius's zero-trust proxy; your keys never sit on disk.
This means you can trust the connection while building complex automations across multiple platforms.
019d8499-f3f3-72a9-9e4e-b5441719ab4c 
Here's how it actually works
The bottom line is: it takes plain text input and returns structured, multi-lingual audio output.
First, you connect your access keys and secret keys to this MCP.
Next, you call the tool with the text and parameters (e.g., voice model, language).
Finally, you receive the generated audio data or a link to the completed file.
Who is this actually for?
Any content producer whose job involves converting written material into spoken word. Think video editors, e-learning developers, or audiobook producers who need rapid, high-quality voiceovers without hiring talent.
Needs to generate multiple voice tracks for Reels, TikToks, and YouTube shorts quickly. They rely on the ability to select specific styles, like the iconic TikTok voices.
Converts course manuals and educational articles into accessible audio modules for their students, often needing multi-language support.
Requires synthesizing long-form text from manuscripts while maintaining natural pacing and consistency across hundreds of chapters.
What Changes When You Connect
Need a specific sound? You can use list_voices to check every available model, from general narrators to the famous TikTok voices, before you write a single line of code.
Dealing with massive documents? Forget manual splitting. Use synthesize_long_text for articles and manuals that exceed standard character limits, keeping your workflow continuous.
Want maximum control over the narrative flow? Instead of just sending text, use synthesize_ssml to programmatically insert pauses, changes in tone, or specific emphasis points.
Need a unique brand sound? Train a custom voice using create_custom_voice. You feed it 10-50 recordings, and you get a proprietary voice model for your product.
Speed is key. The ability to adjust speech rate and volume with synthesize_speech means you can tweak the delivery of any piece without re-recording anything.
See it in action
Building a multi-lingual training module
An e-learning developer needs to create course material in English and Japanese. They use list_voices to confirm language support, then call synthesize_speech multiple times with different voice IDs for each language.
Creating an automated podcast chapter
A podcaster writes a 5,000-word transcript. Instead of manually segmenting it, they use synthesize_long_text, which handles the chunking and synthesis process automatically, giving them full narration.
Improving accessibility in an app
A developer is building a medical guide app. They need to ensure complex terms are read with perfect emphasis and pacing, so they use synthesize_ssml tags around the critical phrases.
Launching branded content
A marketing team wants their video ads to feature a specific brand voice. They first run through create_custom_voice, and once approved, they use that custom voice in all subsequent calls to synthesize_speech.
The honest tradeoffs
Calling TTS for short bursts repeatedly
Sending 50 small blocks of text via synthesize_speech one after the other. This is slow, inefficient, and increases API call overhead.
If you're doing a large amount of content, always check if your text exceeds the standard limit; if it does, use synthesize_long_text. If you are creating many small clips, remember to first run get_audio_formats so you know exactly what output format is best for batch processing.
Ignoring voice availability
Trying to synthesize speech using a specific TikTok style or language without knowing the exact model ID. The call will fail with an unrecognized voice error.
Before calling any synthesis tool, always run list_voices. This shows you all available models and confirms if your target voice—whether it's English or Chinese—is active in the catalog.
Not checking job status
Calling a long-form synthesis tool like synthesize_long_text and assuming the audio is immediately available, leading to failed calls because the task isn't done.
After initiating a large synthesis job, use get_task_status. This lets you check if the background process is still 'processing,' or if it has successfully 'completed'.
When It Fits, When It Doesn't
Use this MCP if your primary need is generating high-quality audio narration from text in multiple languages. It handles everything from basic speech to complex, custom voice training. However, don't use it if you only need to transcribe existing audio files; that requires a different type of tool entirely. If you are building an automation that spans several systems—say, taking data from a CRM and turning the summary into an audiobook—you'll want to combine this MCP with other tools in the Vinkius catalog. Because we run everything through Vinkius, your agent can manage these complex audio workflows securely, without having to worry about where or how the credentials are stored.
Don't use this if you only need a single, fixed voice (like basic reading) and have no plans for scale. In that case, a simpler, dedicated text-to-speech API might suffice. But if quality, customization, and global reach matter, this MCP is the one.
Questions you might have
Does `synthesize_speech` support TikTok voices? +
Yes, the core synthesis function supports specific voice styles, including the famous TikTok models. You can select these via the available voice IDs to add trending flair to your content.
How do I make my own brand voice? Use `create_custom_voice`. +
You need 10-50 high-quality recordings of a single speaker. The tool trains the model over 1 to 3 days, giving you an exclusive voice for your brand.
`synthesize_long_text` vs `synthesize_speech`, which should I use? +
If your text is short (under 1024 characters), use synthesize_speech. If you're working with full articles, reports, or documentation, always use the dedicated synthesize_long_text tool.
What if I need to control pauses in my audio? Use `synthesize_ssml`. +
The specialized SSML function lets you embed tags like <break> and <emphasis>. This gives granular control over the timing, pitch, and intonation that basic text synthesis can't manage.
Can I see what voices are available first? Use `list_voices`. +
Running list_voices is essential. It pulls all current voice models—male, female, child, and style-specific options—so you can build your script around known capabilities.
When I use `get_audio_formats`, what's the difference between MP3 and WAV for my project? +
MP3 is best for delivery. It compresses audio, making it small enough for web streaming or apps without losing too much quality. If you need to edit the file later, stick with WAV; it keeps the raw, uncompressed data.
If I run a long synthesis job using `synthesize_speech`, how do I check its progress with `get_task_status`? +
You must pass the unique task ID returned by the initial request to get_task_status. This tool lets you poll the system to see if the process is pending, running, or if it failed completely.
Does `synthesize_speech` let me control the reading speed or volume of the generated audio? +
Yes, you can adjust both. The synthesis call accepts parameters for rate and volume. This lets your agent dynamically modify how fast or loud the final narration sounds.
What makes Volcengine TTS different from other TTS services? +
Volcengine powers the iconic TikTok TTS effects used in billions of videos. It offers industry-leading Chinese speech quality, trendy social media voices, and ByteDance's proprietary neural voice technology.
Which languages are supported? +
Chinese (Mandarin), English, Japanese, and more. Use language parameter: 'zh' for Chinese, 'en' for English, 'ja' for Japanese. Each language has multiple voice styles.
What's the max text length? +
Standard synthesis supports up to 1024 characters per request. For longer texts, use the synthesize_long_text tool which automatically handles chunking and combining results for articles and audiobooks.
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