Designing MCP tools that don't blow up your agent's context window
Learn how to optimize Model Context Protocol (MCP) tools to prevent oversized responses from exhausting your AI agent's context window.
All
P-Video-Edit is Pruna AI’s instruction-driven video editing model. Upload a source video up to 15 seconds long and give one text instruction; the model then produces an edited video. You can also optionally include up to four reference images to support reference-based edits.
Runpod has a public endpoint available for this model. This gives you instant API access to a pre-deployed API for calling this endpoint. You don’t need deployment or infrastructure; all you need is to create an API key and make a request.
How to get started with P-Video-Edit
On Runpod’s Playground
To get started, you can use the Playground. If you click on The Hub, under “Public Endpoints,” you will see a listing for “P-Video-Edit”. You can edit parameters, add your own images, and make the request directly.
The response you get back should look like this once it’s completed:
{
"delayTime": 8607,
"executionTime": 252132,
"id": "sync-a574c4ff-2001-4b59-b689-488ea7363ab0-u2",
"output": {
"cost": 0.2268,
"result": "https://image.runpod.ai/p-video/edit/a920b5c19c714e3d9f2af475fbd34049/result.mp4"
},
"status": "COMPLETED",
"workerId": "phsmefxookxvwp"
}Curl request
To get started making requests with this endpoint, a simple way is to use Curl. Be sure to replace YOUR_API_KEY with your own API key.
curl -X POST https://api.runpod.ai/v2/p-video-edit/run \
-H 'Content-Type: application/json' \
-H 'Authorization: Bearer YOUR_API_KEY' \
-d '{"input":{"prompt":"Add the roof cargo box shown in the reference image to the SUV. Match its shape, matte-black material and proportions. Keep it rigidly attached and correctly aligned to the vehicle roof throughout the camera movement. Preserve the vehicle body, windows, trim, wheels, pedestrian, environment and lighting.","video":"https://image.runpod.ai/test/pruna/p-video-edit/test.mp4","prompt_upsampling":true,"draft":false,"save_audio":true,"seed":0,"images":["https://image.runpod.ai/uploads/907XnLTSFm/f876233c-8d67-45b5-a347-9b063795fe74.png","https://image.runpod.ai/uploads/dWeJxLfk_B/b8fa8e1c-0f75-43c2-9440-4c6ec15c5183.png","https://image.runpod.ai/uploads/qq7zw1TW9m/567cbf13-0745-4e38-879b-f730a00d5d76.png"]}}'Python
To get started with Python, you can use the requests package. It’s important to note that this model isn’t compatible with OpenAI’s SDK.
import requests
url = "https://api.runpod.ai/v2/p-video-edit/run"
headers = {
"Content-Type": "application/json",
"Authorization": "Bearer YOUR_API_KEY",
}
payload = {
"input": {
"prompt": (
"Add the roof cargo box shown in the reference image to the SUV. "
"Match its shape, matte-black material and proportions. Keep it "
"rigidly attached and correctly aligned to the vehicle roof throughout "
"the camera movement. Preserve the vehicle body, windows, trim, wheels, "
"pedestrian, environment and lighting."
),
"video": "https://image.runpod.ai/test/pruna/p-video-edit/test.mp4",
"prompt_upsampling": True,
"draft": False,
"save_audio": True,
"seed": 0,
"images": [
"https://image.runpod.ai/uploads/907XnLTSFm/f876233c-8d67-45b5-a347-9b063795fe74.png",
"https://image.runpod.ai/uploads/dWeJxLfk_B/b8fa8e1c-0f75-43c2-9440-4c6ec15c5183.png",
"https://image.runpod.ai/uploads/qq7zw1TW9m/567cbf13-0745-4e38-879b-f730a00d5d76.png",
],
}
}
response = requests.post(url, headers=headers, json=payload)
response.raise_for_status()
print(response.json())Conclusion
P-Video-Edit is Pruna AI’s instruction-driven video editing model that transforms videos up to 15 seconds using a single text instruction, with optional support for up to four reference images. Be sure to let us know on Discord, in the #built-on-runpod channel, if you build anything with it.
