semvec/pypi-packages huggingface dataset:
iflow-mcp_cartesia-ai_cartesia-mcp 0.1.6
The official Cartesia MCP server
Requires Python: >=3.11

# Cartesia MCP Server

The Cartesia MCP server provides a way for clients such as Cursor, Claude Desktop, and OpenAI agents to interact with Cartesia's API. Users can localize speech, convert text to audio, infill voice clips etc. 

## Cartesia Setup 

Ensure that you have created an account on [Cartesia](https://play.cartesia.ai/sign-in), there is a free tier with 20,000 credits per month. Once in the Cartesia playground, create an API key under API Keys --> New.

## Installation

```sh
pip install cartesia-mcp
which cartesia-mcp # absolute path to executable
```

## Claude Desktop Integration

Add the following to `claude_desktop_config.json` which can be found through Settings --> Developer --> Edit Config.

```
{
  "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)
      }
    }
  }
}
```

Try asking Claude to 
- List all available Cartesia voices
- To convert a text phrase into audio using a particular voice
- To localize an existing voice into a different language
- To infill audio between two existing audio segments (specify absolute paths to audio files)
- To change an audio file to use a different voice

## Cursor Integration

Create either a `.cursor/mcp.json` in your project or a global `~/.cursor/mcp.json`. The same config as for Claude can be used.
receptiviti 0.2.2
Process texts with the Receptiviti API
Requires Python: >=3.8

A Python package to process text with the [Receptiviti](https://www.receptiviti.com) API.

An R package is also available at [Receptiviti/receptiviti-r](https://receptiviti.github.io/receptiviti-r/).

## Installation

If needed, download Python from [python.org](https://www.python.org/downloads), then install the package with pip:

Release ([version 0.2.2](https://pypi.org/project/receptiviti/0.2.2))

```sh
pip install receptiviti
```

Development

```sh
pip install git+https://github.com/receptiviti/receptiviti-python.git
```

And load the package in a Python console:

```py
import receptiviti
```

## Examples

```py
# score a single text
single = receptiviti.request("a text to score")

# score multiple texts, and write results to a file
multi = receptiviti.request(["first text to score", "second text"], "filename.csv")

# score texts in separate files
## defaults to look for .txt files
file_results = receptiviti.request(directory = "./path/to/txt_folder")

## could be .csv
file_results = receptiviti.request(
  directory = "./path/to/csv_folder",
  text_column = "text", file_type = "csv"
)

# score texts in a single file
results = receptiviti.request("./path/to/file.csv", text_column = "text")
```

## API Access

To access the API, you will need to load your key and secret, as found on your [dashboard](https://dashboard.receptiviti.com).

You can enter these as arguments in each function call, but by default they will be looked for in these environment variables:

```
RECEPTIVITI_KEY="32lettersandnumbers"
RECEPTIVITI_SECRET="56LettersAndNumbers"
```

You can store these in a `.env` file (in the current directory or `~/Documents`) permanently, or set them temporarily:

```py
import os
os.environ["RECEPTIVITI_KEY"]="32lettersandnumbers"
os.environ["RECEPTIVITI_SECRET"]="56LettersAndNumbers"
```