# Demo
This demo shows how to use the Union MCP server to run compute- and io-intensive
tools on a Flyte cluster.
## Setup
Take a look at the [mcp configuration guide](./guides/mcp_configuration.md) for more detailed instructions on how to
configure your MCP client to use the Union MCP server.
### Cursor
Add the following to your `~/.cursor/mcp.json` file:
```json
{
"mcpServers": {
"union-mcp-v2": {
"url": "https://mcp-v2.apps.demo.hosted.unionai.cloud/sdk/mcp"
"headers": {
"Authorization": "Bearer <secret-value>"
}
}
}
}
```
### Claude Code
Add the following to your `~/.claude.json` file:
```json
{
"mcpServers": {
"union-mcp-v2": {
"url": "https://mcp-v2.apps.demo.hosted.unionai.cloud/sdk/mcp"
"headers": {
"Authorization": "Bearer <secret-value>"
}
}
}
}
```
### Claude Desktop
Configure the `claude_desktop_config.json` configuration file located in:
- macOS: `~/Library/Application Support/Claude/claude_desktop_config.json`
- Windows: `%APPDATA%\Claude\claude_desktop_config.json`
Make sure that `npx` is installed and available in your `$PATH`.
```json
{
"mcpServers": {
"union-mcp-v2": {
"command": "npx",
"args": [
"mcp-remote",
"https://mcp-v2.apps.demo.hosted.unionai.cloud/sdk/mcp",
"--header",
"Authorization: Bearer ${AUTH_TOKEN}"
],
"env": {
"AUTH_TOKEN": "<secret-value>"
}
}
}
}
```
## Prompts
### Toy example
> Run a flyte script remotely, which fans out tasks to compute the square of the numbers from 1 to 100_000, where each task handles 1_000 numbers, then sums the squares.
### Data visualization
> Run a flyte script that downloads the dataset at https://github.com/plotly/datasets/blob/master/timeseries.csv and creates a visualization in plotly and run it remotely. The flyte script should use flyte.report to render a beautiful visualization.
### Classical ML model training
> Run a flyte script that performs hyperparameter optimization that uses flyte to parallelize the training runs for training a random forest model on the penguins data. Assess f1 score as the evaluation metric, and visualize the results using flyte.report. Make sure the report style is beautiful.
### PDF text to speech
> Run a flyte script that grabs the 5 most recently published articles from Arxiv and generates audio files from article text using suno/bark-small using the huggingface transformers library. Use a driver-worker pattern where the driver is a CPU environment and the worker is a GPU environment with one T4 GPU. Save the audio files to a json file using flyte.io.File, and use flyte.report make a pretty visualization of the audio files, including a preview of text the contents of documents the audio files embedded in the html report.
### GPU Batch inference for RAG
> Run a flyte script that embeds the "review" column of the "scikit-learn/imdb" huggingface dataset using the "answerdotai/ModernBERT-base" model on a T4 GPU. Use a driver-worker pattern where the driver is a CPU environment and the worker is a GPU environment. Save the embeddings to a json file using flyte.io.File, and use flyte.report make a pretty visualization of the embeddings, including a preview of text the contents of the first five documents and the distribution of their embeddings.
