How can I preview the output without fully committing my prompt?

Video generation actions using the Runway engines are directly invoked through asynchronous background tasks. Instead of an exact real-time preview, you can start small 5-second generations or iterate closely on your prompt texts until they produce the final aesthetic you want.

Does this integration process video rendering in real-time?

No. Video rendering is asynchronous. When you submit a prompt or an image, the AI will dispatch a background task to Runway's servers and receive a 'task_id'. You will need to ask the AI to verify the status of this task later, usually after a few minutes, to fetch the final video URL.

Can I use Gen-4 models for free?

No. Runway requires credits for all generations, and Gen-4 Turbo consumes significantly more credits than earlier models or standard actions. You must maintain sufficient active credits or a paid subscription on your Runway account for the AI to successfully execute the rendering jobs.

Runway ML MCP Server for Google ADK 10 tools — connect in under 2 minutes

Name: Runway ML
Availability: InStock
Author: Vinkius

google.github.io/adk-docs/tools/mcp-tools

Built by Vinkius GDPR 10 Tools SDK

Google Agent Development Kit (ADK) is Google's framework for building production AI agents. Add Runway ML as an MCP tool provider through the Vinkius and your ADK agents can call every tool with full schema introspection.

Get MCP Server for AI Agents

ASK AI ABOUT THIS MCP SERVER

Open in ChatGPT Open in Claude Open in Perplexity

Vinkius supports streamable HTTP and SSE.

python

from google.adk.agents import Agent
from google.adk.tools.mcp_tool import McpToolset
from google.adk.tools.mcp_tool.mcp_session_manager import (
    StreamableHTTPConnectionParams,
)

# Your Vinkius token — get it at cloud.vinkius.com
mcp_tools = McpToolset(
    connection_params=StreamableHTTPConnectionParams(
        url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp",
    )
)

agent = Agent(
    model="gemini-2.5-pro",
    name="runway_ml_agent",
    instruction=(
        "You help users interact with Runway ML "
        "using 10 available tools."
    ),
    tools=[mcp_tools],
)

Fully ManagedVinkius Servers

60%Token savings

High SecurityEnterprise-grade

IAMAccess control

EU AI ActCompliant

DLPData protection

V8 IsolateSandboxed

Ed25519Audit chain

<40msKill switch

Stream every event to Splunk, Datadog, or your own webhook in real-time

* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure

About Runway ML MCP Server

Connect your AI to Runway ML, the pioneer in applied AI research shaping the next era of art, entertainment and human creativity. This powerful integration empowers you to tap directly into Runway's cutting-edge Gen-3 Alpha and Gen-4 video generation models right from your conversational workspace. Produce stunning, realistic, or highly stylized video clips simply by typing out your vision or providing a reference image.

Google ADK natively supports Runway ML as an MCP tool provider — declare the Vinkius Edge URL and the framework handles discovery, validation, and execution automatically. Combine 10 tools with Gemini's long-context reasoning for complex multi-tool workflows, with production-ready session management and evaluation built in.

What you can do

Text-to-Video Generation — Write detailed prompts to synthesize entirely new, imaginative scenes using gen3_turbo, gen4_turbo, or the standard text_to_video tooling.
Image-to-Video Animation — Bring still images to life using image_to_video or precisely guide the motion of a starting image with a textual director prompt using image_text_to_video.
Advanced Interpolation — Seamlessly blend two distinct keyframe images into one smooth transitional motion clip (interpolate).
Complete Task Management — Maintain full control over costly generation pipelines. Easily check job status or output URLs (get_task, list_tasks), cancel ongoing renders to save credits (cancel_task), and audit your organization's billing usage (get_organization).

The Runway ML MCP Server exposes 10 tools through the Vinkius. Connect it to Google ADK in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.

How to Connect Runway ML to Google ADK via MCP

Follow these steps to integrate the Runway ML MCP Server with Google ADK.

Install Google ADK

Run pip install google-adk

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token

Create the agent

Save the code above and integrate into your ADK workflow

Explore tools

The agent will discover 10 tools from Runway ML via MCP

Why Use Google ADK with the Runway ML MCP Server

Google ADK provides unique advantages when paired with Runway ML through the Model Context Protocol.

Google ADK natively supports MCP tool servers — declare a tool provider and the framework handles discovery, validation, and execution

Built on Gemini models, ADK provides long-context reasoning ideal for complex multi-tool workflows with Runway ML

Production-ready features like session management, evaluation, and deployment come built-in — not bolted on

Seamless integration with Google Cloud services means you can combine Runway ML tools with BigQuery, Vertex AI, and Cloud Functions

Runway ML + Google ADK Use Cases

Practical scenarios where Google ADK combined with the Runway ML MCP Server delivers measurable value.

Enterprise data agents: ADK agents query Runway ML and cross-reference results with internal databases for comprehensive analysis

Multi-modal workflows: combine Runway ML tool responses with Gemini's vision and language capabilities in a single agent

Automated compliance checks: schedule ADK agents to query Runway ML regularly and flag policy violations or configuration drift

Internal tool platforms: build self-service agent platforms where teams connect their own MCP servers including Runway ML

Runway ML MCP Tools for Google ADK (10)

These 10 tools become available when you connect Runway ML to Google ADK via MCP:

cancel_task

This action is irreversible. Cancels a running generation task

gen3_turbo

Quick 5-second video generation using Gen-3 Alpha Turbo

gen4_turbo

High-quality video generation using Gen-4 Turbo

get_organization

Retrieves Runway ML organization and credit details

get_task

Look for SUCCEEDED status and output URL. Retrieves the status and output of a generation task

image_text_to_video

Generates video from both an image and a text prompt

image_to_video

Specify source image URL, model, and duration. Animates a still image into a video

interpolate

Creates smooth motion between two keyframe images

list_tasks

Lists recent generation tasks

text_to_video

Specify prompt, model, and duration (5 or 10). Returns a task ID. Generates a video from a text prompt

Example Prompts for Runway ML in Google ADK

Ready-to-use prompts you can give your Google ADK agent to start working with Runway ML immediately.

"Create a 5 second cinematic video showing a sunset over an alien planet using Runway Gen-3 Turbo."

"Take this reference image URL and animate it with Gen-3 Turbo to make the camera slowly pan backwards."

"List all my ongoing tasks on Runway to see if the video has finished rendering."

Troubleshooting Runway ML MCP Server with Google ADK

Common issues when connecting Runway ML to Google ADK through the Vinkius, and how to resolve them.

McpToolset not found

Update: pip install --upgrade google-adk

Runway ML + Google ADK FAQ

Common questions about integrating Runway ML MCP Server with Google ADK.

How does Google ADK connect to MCP servers?

Import the MCP toolset class and pass the server URL. ADK discovers and registers all tools automatically, making them available to your agent's tool-use loop.

Can ADK agents use multiple MCP servers?

Yes. Declare multiple MCP tool providers in your agent configuration. ADK merges all tool schemas and the agent can call tools from any server in a single turn.

Which Gemini models work best with MCP tools?

Gemini 2.0 Flash and Pro models both support function calling required for MCP tools. Flash is recommended for latency-sensitive use cases, Pro for complex reasoning.