TextToSpeechSkills
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Text-to-speech MCP

Text-to-speech MCP tools for LLM apps

TextToSpeechSkills gives LLM apps a focused MCP toolset for turning scripts into speech. The agent can validate expression tags, choose approved voice templates, check credit use, create jobs, and return audio links without touching broad account settings.

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Who is this for?

TextToSpeechSkills is a text-to-speech MCP platform for teams that want LLM apps to create audio safely. The MCP server exposes narrow speech tools for validating readable expression tags, listing approved voice templates, previewing credit use, creating async speech jobs, and returning audio URLs. Non-technical users can connect it with a copy-and-paste setup, while developers still get a clean API when the same workflow moves into a product backend. The domain texttospeechskills.com also matches the workflow: text-to-speech skills, MCP tools, reusable voices, and simple setup for LLM users.

Easy LLM setup

LLM-ready even for non-technical teams

Setup is intentionally short: create a scoped key, copy the MCP install command, choose which voice templates the LLM may use, and ask for audio from chat.

Read setup guide
01Create a scoped key
02Install MCP
03Choose a voice template
04Generate audio from chat

Install one focused speech tool

The LLM app gets purpose-built speech actions instead of broad account access, so setup feels simple and permissions stay easy to explain.

Validate text before audio

Agents can check expression tags, preview credit use, and correct unsupported directions before a speech job uses credits.

Use approved voice templates

Template names keep narrators, characters, and support voices consistent while letting the LLM handle each script.

When this helps

Teams connecting LLM apps, desktop agents, and AI workspaces to text-to-speech usually need a repeatable path for writing, review, generation, billing, and reuse. The most important jobs here are install one focused speech tool, validate text before audio, use approved voice templates. Those are the moments where voice becomes part of real work instead of a one-off export.

How the workflow works

Start with readable text, add expression tags when tone matters, choose an approved voice template, and create a speech job through the UI, API, or MCP. The same pattern works for text-to-speech MCP, text to speech MCP, MCP text-to-speech, LLM speech tools, which makes it easier for humans and LLM apps to share one process without exposing internal routing or credentials.

Before you roll it out

Decide which templates are approved, which expression tags are allowed, who can create workspace keys, and which usage limits are acceptable. Those choices keep automated voice generation useful without letting it sprawl from the first paid Test plan through Pro, Scale, and Business usage.

Common questions

What teams usually ask before starting

These are the practical details that matter before a team adds speech generation to a real workflow.

Who should use Text-to-Speech MCP for LLM Apps?

Teams connecting LLM apps, desktop agents, and AI workspaces to text-to-speech should use this page when they want generated speech that is easy to review, consistent across prompts, and simple to connect to LLM tools. The core workflow combines expression tags, voice templates, credit previews, and job-based generation.

Can a non-technical user connect this to an LLM app?

Setup is intentionally short: create a scoped key, copy the MCP install command, choose which voice templates the LLM may use, and ask for audio from chat. The setup guide keeps the first path short while still giving developers a clean API when the workflow moves into a product backend.

How does pricing stay predictable?

Every paid plan uses credits. Teams can add credit packs when needed, and workspaces on Pro and higher add central billing for $2 per user per month.

Related reading

Keep exploring TextToSpeechSkills

Use these guides to move from a first audio test to a repeatable workflow for your team.

LLM Text-to-Speech Setup GuideA simple guide for connecting TextToSpeechSkills to LLM apps with MCP, scoped keys, voice templates, and expression tags.Text-to-Speech Skills for LLM WorkflowsInstall text-to-speech skills that teach LLM apps how to use expression tags, saved voices, MCP tools, and speech jobs safely.TTS MCP ServerInstall an MCP server that lets LLM agents validate expression markup, check credit use, use voice templates, and create speech jobs.Expressive TTS Markup for Product CopyGuide tone, pace, and delivery with simple inline tags like [quiet] and [loud and angry] before generating speech.How to Add Text-to-Speech to an LLM AppA practical launch guide for adding text-to-speech to LLM apps with expression tags, voice templates, MCP setup, jobs, and team billing.Expression Tags for Better AI Voice OutputLearn how expression tags make text-to-speech scripts easier to review, automate, and reuse across LLM apps, creators, games, and support flows.

API playground

Plain JSON in, speech job out

{
  "text": "[quiet] hello. [loud and angry] how are you?",
  "voice_template": "vt_calm_narrator_v1",
  "generation_mode": "instant",
  "format": "mp3"
}
202 queued for polling200 audio ready

MCP install

Agent tools included at launch

Claude Desktoppnpm --package texttospeechskills dlx tts-skills-mcp
Codexpnpm --package texttospeechskills dlx tts-skills-mcp
Cursorpnpm --package texttospeechskills dlx tts-skills-mcp
Skills helperpnpm --package texttospeechskills dlx tts-skills tags