Examples for using text-to-speech-multilingual-v2 through RunAPI from agent tools or API calls. Copy a prompt, then use it in Claude Code, Codex, Cursor, Windsurf, or your backend.
1. claude mcp add runapi -s user -- npx -y @runapi.ai/mcp
2. Restart Claude Code
3. Paste this prompt: Generate audio: "Hold on, let me think. <break time="1.5s" /> Alright, I've got it. The key to understanding this phenomenon is not in the data itself, but in what the data leaves out."
1. codex plugin install runapi-mcp@agents
2. Restart Codex
3. Paste this prompt: Generate audio: "Hold on, let me think. <break time="1.5s" /> Alright, I've got it. The key to understanding this phenomenon is not in the data itself, but in what the data leaves out."
1. npx @runapi.ai/mcp init cursor
2. Restart Cursor
3. Paste this prompt: Generate audio: "Hold on, let me think. <break time="1.5s" /> Alright, I've got it. The key to understanding this phenomenon is not in the data itself, but in what the data leaves out."
1. npx @runapi.ai/mcp init windsurf
2. Restart Windsurf
3. Paste this prompt: Generate audio: "Hold on, let me think. <break time="1.5s" /> Alright, I've got it. The key to understanding this phenomenon is not in the data itself, but in what the data leaves out."
curl -X POST https://runapi.ai/api/v1/elevenlabs/text_to_sound \
-H "Authorization: Bearer $RUNAPI_KEY" \
-H "Content-Type: application/json" \
--data-binary @- <<'JSON'
{
"model": "text-to-speech-multilingual-v2",
"text": "Hold on, let me think. <break time=\"1.5s\" /> Alright, I've got it. The key to understanding this phenomenon is not in the data itself, but in what the data leaves out."
}
JSON
import { ElevenlabsClient } from "@runapi.ai/elevenlabs";
const client = new ElevenlabsClient({
apiKey: process.env.RUNAPI_API_KEY,
});
const result = await client.textToSound.run({
"model": "text-to-speech-multilingual-v2",
"text": "Hold on, let me think. <break time=\"1.5s\" /> Alright, I've got it. The key to understanding this phenomenon is not in the data itself, but in what the data leaves out."
});
console.log(result.id);
require "runapi/elevenlabs"
client = RunApi::Elevenlabs::Client.new
result = client.text_to_sound.run(
model: "text-to-speech-multilingual-v2",
text: "Hold on, let me think. <break time=\"1.5s\" /> Alright, I've got it. The key to understanding this phenomenon is not in the data itself, but in what the data leaves out."
)
puts result.id
package main
import (
"context"
"fmt"
"log"
"net/http"
"os"
"strings"
)
func main() {
body := strings.NewReader("{\"model\":\"text-to-speech-multilingual-v2\",\"text\":\"Hold on, let me think. <break time=\\\"1.5s\\\" /> Alright, I've got it. The key to understanding this phenomenon is not in the data itself, but in what the data leaves out.\"}")
req, err := http.NewRequestWithContext(context.Background(), http.MethodPost, "https://runapi.ai/api/v1/elevenlabs/text_to_sound", body)
if err != nil {
log.Fatal(err)
}
req.Header.Set("Authorization", "Bearer "+os.Getenv("RUNAPI_API_KEY"))
req.Header.Set("Content-Type", "application/json")
resp, err := http.DefaultClient.Do(req)
if err != nil {
log.Fatal(err)
}
defer resp.Body.Close()
fmt.Println(resp.Status)
}
text-to-speech-multilingual-v2/api/v1/elevenlabs/text_to_soundGet API Key
The old lighthouse keeper climbed the spiral staircase one last time. Each iron step rang out beneath his boots, echoing off the curved stone walls as it had for thirty-seven years. At the top, he paused to catch his breath and looked out across the Atlantic. The beam would spin tonight without him. Tomorrow, the automation engineers would arrive, and the light would keep itself. He pressed his palm flat against the cold glass of the lantern room and whispered goodbye to the sea.
curl -X POST https://runapi.ai/api/v1/elevenlabs/text_to_sound \
-H "Authorization: Bearer $RUNAPI_KEY" \
-H "Content-Type: application/json" \
--data-binary @- <<'JSON'
{
"model": "text-to-speech-multilingual-v2",
"text": "The old lighthouse keeper climbed the spiral staircase one last time. Each iron step rang out beneath his boots, echoing off the curved stone walls as it had for thirty-seven years. At the top, he paused to catch his breath and looked out across the Atlantic. The beam would spin tonight without him. Tomorrow, the automation engineers would arrive, and the light would keep itself. He pressed his palm flat against the cold glass of the lantern room and whispered goodbye to the sea."
}
JSON
FAQ
Using text-to-speech-multilingual-v2 prompts
What is %{model}?
%{model} is available through RunAPI as part of the unified model catalog. These prompts show practical input patterns that agents and backend services can reuse.
How do I use these prompts?
Copy any prompt and paste it into Claude Code, Codex, Cursor, or Windsurf after installing the RunAPI MCP Server. Developers can also copy the API example and send the prompt directly.
Do these prompts cost money to browse?
Browsing and copying prompt examples is free. Generation requests only cost money when you call a RunAPI model with your API key.
Can I adapt the prompts for production?
Yes. Treat each prompt as a starting point, then add your brand rules, output dimensions, safety constraints, and application-specific context before using it in production.