Cartesia MCP Server Integration

What does “Cartesia” MCP Server do?

The Cartesia MCP (Model Context Protocol) Server acts as a bridge that allows AI assistants and clients—such as Cursor, Claude Desktop, and OpenAI agents—to interact with Cartesia’s API. This enables enhanced development workflows by providing tools for speech localization, converting text to audio, infilling voice clips, and more. By integrating with Cartesia MCP, developers can automate and standardize the generation, manipulation, and localization of audio content, thereby streamlining tasks that require voice synthesis and advanced audio operations. The server plays a critical role in expanding what AI agents can do by exposing Cartesia’s specialized voice and audio capabilities through a unified MCP interface.

List of Prompts

No prompt templates are mentioned in the repository or documentation.

List of Resources

No explicit resources are documented in the available files or README.

List of Tools

No explicit list of tools or server.py file is available in the repository to enumerate tools.

Use Cases of this MCP Server

• Voice Listing: Enables developers to list all available Cartesia voices, making it easy to explore and select different voice models directly from their AI client.
• Text-to-Audio Conversion: Allows conversion of text phrases into audio using a specified voice, automating the creation of audio assets for applications, chatbots, or accessibility features.
• Voice Localization: Facilitates the localization of existing voices into different languages, supporting multilingual content generation and internationalization efforts.
• Audio Infill: Supports infilling of audio between two existing segments, enabling seamless audio editing and enhancing creative workflows for podcasts, games, or digital media.
• Voice Change for Audio Files: Lets users change the voice in an existing audio file, useful for rapid prototyping, voice cloning, or customizing user experiences.

How to set it up

Windsurf

No setup instructions available for Windsurf.

Claude

1. Ensure you have an account on Cartesia and have generated your API key.
2. Install the package: pip install cartesia-mcp
3. Locate the claude_desktop_config.json file via Settings → Developer → Edit Config.
4. Add the following JSON snippet to the mcpServers section:

   {
     "mcpServers": {
       "cartesia-mcp": {
         "command": "<absolute-path-to-executable>",
         "env": {
           "CARTESIA_API_KEY": "<insert-your-api-key-here>",
           "OUTPUT_DIRECTORY": "// directory to store generated files (optional)"
         }
       }
     }
   }
   

5. Save the configuration and restart Claude Desktop to apply changes.

Securing API Keys:
Use environment variables in the env field of your config as above.

Cursor

1. Ensure you have an account on Cartesia and have generated your API key.
2. Install the package: pip install cartesia-mcp
3. Create a .cursor/mcp.json in your project directory or ~/.cursor/mcp.json for global config.
4. Use the same configuration as Claude (see above).
5. Save and restart Cursor as needed.

Securing API Keys:
Use environment variables in the env field of your config as above.

Cline

No setup instructions available for Cline.

How to use this MCP inside flows

Using MCP in FlowHunt

To integrate MCP servers into your FlowHunt workflow, start by adding the MCP component to your flow and connecting it to your AI agent:

Click on the MCP component to open the configuration panel. In the system MCP configuration section, insert your MCP server details using this JSON format:

{
  "cartesia-mcp": {
    "transport": "streamable_http",
    "url": "https://yourmcpserver.example/pathtothemcp/url"
  }
}

Once configured, the AI agent is now able to use this MCP as a tool with access to all its functions and capabilities. Remember to change “cartesia-mcp” to whatever the actual name of your MCP server is and replace the URL with your own MCP server URL.

Overview

| Roots Support | ⛔ | No mention of roots |

How would we rate this MCP server?
The Cartesia MCP Server provides straightforward integration for audio and voice tasks and clear setup instructions for popular AI clients. However, it lacks documentation on available tools, resources, prompts, and advanced MCP features like roots and sampling. Based on the above, we would rate its MCP implementation as a 3/10 on completeness and utility for the protocol.

MCP Score