ai-mldeveloper-tools

Next.MetadataOutlet

hammeiam

by hammeiam

Kokoro Speech: natural-sounding Kokoro TTS with customizable voices and playback speed — fast, reliable text-to-speech w

Provides text-to-speech capabilities using the Kokoro TTS model, enabling natural-sounding voice output with customizable playback speed and voice selection through robust error handling and temporary file management.

github stars

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Both formats append explainx.ai attribution and the canonical URL for this MCP server listing.

No API key requiredHigh-quality Kokoro TTS modelMultiple voice options

best for

  • / Adding voice output to applications
  • / Creating audio content from text
  • / Accessibility features for reading text aloud

capabilities

  • / Convert text to speech with multiple voice options
  • / Adjust speech speed from 0.5x to 2.0x
  • / List all available TTS voices
  • / Check TTS model initialization status

what it does

Converts text to natural-sounding speech using the Kokoro TTS model with customizable voice selection and playback speed.

how to install

You can install Kokoro Speech in your AI client of choice. Use the install panel on this page to get one-click setup for Cursor, Claude Desktop, VS Code, and other MCP-compatible clients. This server runs locally on your machine via the stdio transport.

license

MIT

Kokoro Speech is released under the MIT license. This is a permissive open-source license, meaning you can freely use, modify, and distribute the software.

readme

Speech MCP Server

A Model Context Protocol server that provides text-to-speech capabilities using the Kokoro TTS model.

Configuration

The server can be configured using the following environment variables:

VariableDescriptionDefaultValid Range
MCP_DEFAULT_SPEECH_SPEEDDefault speed multiplier for text-to-speech1.10.5 to 2.0
MCP_DEFAULT_VOICEDefault voice for text-to-speechaf_bellaAny valid voice ID

In Cursor:

{
  "mcpServers": {
    "speech": {
      "command": "npx",
      "args": [
        "-y",
        "speech-mcp-server"
      ],
      "env": {
        "MCP_DEFAULT_SPEECH_SPEED": 1.3,
        "MCP_DEFAULT_VOICE": "af_bella"
      }
    }
  }
}

Features

  • 🎯 High-quality text-to-speech using Kokoro TTS model
  • 🗣️ Multiple voice options available
  • 🎛️ Customizable speech parameters (voice, speed)
  • 🔌 MCP-compliant interface
  • 📦 Easy installation and setup
  • 🚀 No API key required

Installation

# Using npm
npm install speech-mcp-server

# Using pnpm (recommended)
pnpm add speech-mcp-server

# Using yarn
yarn add speech-mcp-server

Usage

Run the server:

# Using default configuration
npm start

# With custom configuration
MCP_DEFAULT_SPEECH_SPEED=1.5 MCP_DEFAULT_VOICE=af_bella npm start

The server provides the following MCP tools:

  • text_to_speech: Basic text-to-speech conversion
  • text_to_speech_with_options: Text-to-speech with customizable speed
  • list_voices: List all available voices
  • get_model_status: Check the initialization status of the TTS model

Development

# Clone the repository
git clone <your-repo-url>
cd speech-mcp-server

# Install dependencies
pnpm install

# Start development server with auto-reload
pnpm dev

# Build the project
pnpm build

# Run linting
pnpm lint

# Format code
pnpm format

# Test with MCP Inspector
pnpm inspector

Available Tools

1. text_to_speech

Converts text to speech using the default settings.

{
  "type": "request",
  "id": "1",
  "method": "call_tool",
  "params": {
    "name": "text_to_speech",
    "arguments": {
      "text": "Hello world",
      "voice": "af_bella"  // optional
    }
  }
}

2. text_to_speech_with_options

Converts text to speech with customizable parameters.

{
  "type": "request",
  "id": "1",
  "method": "call_tool",
  "params": {
    "name": "text_to_speech_with_options",
    "arguments": {
      "text": "Hello world",
      "voice": "af_bella",  // optional
      "speed": 1.0,         // optional (0.5 to 2.0)
    }
  }
}

3. list_voices

Lists all available voices for text-to-speech.

{
  "type": "request",
  "id": "1",
  "method": "list_voices",
  "params": {}
}

4. get_model_status

Check the current status of the TTS model initialization. This is particularly useful when first starting the server, as the model needs to be downloaded and initialized.

{
  "type": "request",
  "id": "1",
  "method": "call_tool",
  "params": {
    "name": "get_model_status",
    "arguments": {}
  }
}

Response example:

{
  "content": [{
    "type": "text",
    "text": "Model status: initializing (5s elapsed)"
  }]
}

Possible status values:

  • uninitialized: Model initialization hasn't started
  • initializing: Model is being downloaded and initialized
  • ready: Model is ready to use
  • error: An error occurred during initialization

Testing

You can test the server using the MCP Inspector or by sending raw JSON messages:

# List available tools
echo '{"type":"request","id":"1","method":"list_tools","params":{}}' | node dist/index.js

# List available voices
echo '{"type":"request","id":"2","method":"list_voices","params":{}}' | node dist/index.js

# Convert text to speech
echo '{"type":"request","id":"3","method":"call_tool","params":{"name":"text_to_speech","arguments":{"text":"Hello world","voice":"af_bella"}}}' | node dist/index.js

Integration with Claude Desktop

To use this server with Claude Desktop, add the following to your Claude Desktop config file (~/Library/Application Support/Claude/claude_desktop_config.json):

{
  "servers": {
    "speech": {
      "command": "npx",
      "args": ["@decodershq/speech-mcp-server"]
    }
  }
}

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

License

MIT License - see the LICENSE file for details.

Troubleshooting

Model Initialization Issues

The server automatically attempts to download and initialize the TTS model on startup. If you encounter initialization errors:

  1. The server will automatically retry up to 3 times with a cleanup between attempts
  2. Use the get_model_status tool to monitor initialization progress and any errors
  3. If initialization fails after all retries, try manually removing the model files:
# Remove model files (MacOS/Linux)
rm -rf ~/.npm/_npx/**/node_modules/@huggingface/transformers/.cache/onnx-community/Kokoro-82M-v1.0-ONNX/onnx/model_quantized.onnx
rm -rf ~/.cache/huggingface/transformers/onnx-community/Kokoro-82M-v1.0-ONNX/onnx/model_quantized.onnx

# Then restart the server
npm start

The get_model_status tool will now include retry information in its response:

{
  "content": [{
    "type": "text",
    "text": "Model status: initializing (5s elapsed, retry 1/3)"
  }]
}

FAQ

What is the Next.MetadataOutlet MCP server?
Next.MetadataOutlet is a Model Context Protocol (MCP) server profile on explainx.ai. MCP lets AI hosts (e.g. Claude Desktop, Cursor) call tools and resources through a standard interface; this page summarizes categories, install hints, and community ratings.
How do MCP servers relate to agent skills?
Skills are reusable instruction packages (often SKILL.md); MCP servers expose live capabilities. Teams frequently combine both—skills for workflows, MCP for APIs and data. See explainx.ai/skills and explainx.ai/mcp-servers for parallel directories.
How are reviews shown for Next.MetadataOutlet?
This profile displays 59 aggregated ratings (sample rows for discoverability plus signed-in user reviews). Average score is about 4.7 out of 5—verify behavior in your own environment before production use.

Use Cases

Extended AI Capabilities

Add new capabilities to Claude beyond text generation

Example

Access external data sources, execute code, interact with tools and services

Transform Claude from chatbot to action-taking agent

Context Enhancement

Provide Claude with access to relevant context and data

Example

Load project documentation, access knowledge bases, query databases

Get more accurate, context-aware responses

Workflow Automation

Automate multi-step workflows combining AI and external tools

Example

Research → Summarize → Create document → Send notification

Complete complex tasks end-to-end without manual steps

Implementation Guide

Prerequisites

  • Claude Desktop 0.7.0+ or Cursor IDE with MCP support
  • Basic understanding of MCP architecture and capabilities
  • Access credentials for integrated services (if required)
  • Willingness to experiment and iterate on configuration

Time Estimate

15-60 minutes depending on server complexity

Installation Steps

  1. 1.Install MCP server: npm install -g [package-name] or via GitHub
  2. 2.Add server configuration to ~/.claude/mcp.json
  3. 3.Provide required credentials and configuration
  4. 4.Restart Claude Desktop to load new server
  5. 5.Test basic functionality with simple prompts
  6. 6.Explore capabilities and experiment with use cases
  7. 7.Document successful patterns for reuse

Troubleshooting

  • MCP server not loading: Check config syntax, verify installation
  • Connection errors: Check network, firewall, credentials
  • Feature not working: Read server docs, check required parameters
  • Performance issues: Monitor resource usage, check for network latency
  • Conflicts with other servers: Check port assignments, namespace collisions

Best Practices

✓ Do

  • +Read server documentation thoroughly before setup
  • +Start with simple use cases to validate functionality
  • +Test in non-production environment first
  • +Monitor resource usage and performance
  • +Keep servers updated for bug fixes and new features
  • +Document configuration for team members
  • +Use environment variables for sensitive configuration

✗ Don't

  • Don't grant overly permissive access to MCP servers
  • Don't skip reading security considerations in docs
  • Don't expose sensitive data without proper controls
  • Don't run untrusted MCP servers without code review
  • Don't ignore error messages—investigate root cause

💡 Pro Tips

  • Combine multiple MCP servers for powerful workflows
  • Create custom MCP servers for your specific needs
  • Share successful configurations with team
  • Use MCP inspector for debugging
  • Join MCP community for tips and troubleshooting

Technical Details

Architecture

Model Context Protocol standardizes how AI hosts (Claude, Cursor) communicate with external tools and data sources through server implementations.

Protocols

  • Model Context Protocol (MCP)
  • JSON-RPC 2.0
  • stdio or HTTP transport

Compatibility

  • Claude Desktop
  • Cursor IDE
  • Custom MCP clients

When to Use This

✓ Use When

Use when you need Claude to access external data, execute actions, or integrate with tools. Best for extending AI capabilities beyond conversation.

✗ Avoid When

Avoid when native integrations exist (use official APIs directly), for real-time critical systems, or when security/compliance requires zero external dependencies.

Integration

  • Tool composition: Chain multiple MCP tools in workflows
  • Context augmentation: Provide AI with relevant external data
  • Action delegation: Let AI execute tasks on external systems
  • Bidirectional sync: Keep AI context and external systems in sync

Discussion

Product Hunt–style comments (not star reviews)
  • No comments yet — start the thread.

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Ratings

4.759 reviews
  • Sophia Huang· Dec 24, 2024

    Next.MetadataOutlet has been reliable for tool-calling workflows; the MCP profile page is a good permalink for internal docs.

  • Hana Mehta· Dec 20, 2024

    Next.MetadataOutlet is a well-scoped MCP server in the explainx.ai directory — install snippets and categories matched our Claude Code setup.

  • Charlotte Harris· Dec 20, 2024

    Useful MCP listing: Next.MetadataOutlet is the kind of server we cite when onboarding engineers to host + tool permissions.

  • Zara Park· Dec 16, 2024

    We evaluated Next.MetadataOutlet against two servers with overlapping tools; this profile had the clearer scope statement.

  • Arya Ndlovu· Dec 16, 2024

    Strong directory entry: Next.MetadataOutlet surfaces stars and publisher context so we could sanity-check maintenance before adopting.

  • Hana Ramirez· Dec 12, 2024

    We wired Next.MetadataOutlet into a staging workspace; the listing’s GitHub and npm pointers saved time versus hunting across READMEs.

  • Pratham Ware· Dec 8, 2024

    I recommend Next.MetadataOutlet for teams standardizing on MCP; the explainx.ai page compares cleanly with sibling servers.

  • Sakshi Patil· Nov 27, 2024

    Strong directory entry: Next.MetadataOutlet surfaces stars and publisher context so we could sanity-check maintenance before adopting.

  • Luis Anderson· Nov 15, 2024

    Next.MetadataOutlet reduced integration guesswork — categories and install configs on the listing matched the upstream repo.

  • Tariq Johnson· Nov 11, 2024

    We wired Next.MetadataOutlet into a staging workspace; the listing’s GitHub and npm pointers saved time versus hunting across READMEs.

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