Runway ML MCP Server for LlamaIndex 10 tools — connect in under 2 minutes
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add Runway ML as an MCP tool provider through Vinkius and your agents can query, analyze, and act on live data alongside your existing indexes.
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import asyncio
from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI
async def main():
# Your Vinkius token. get it at cloud.vinkius.com
mcp_client = BasicMCPClient("https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")
mcp_tool_spec = McpToolSpec(client=mcp_client)
tools = await mcp_tool_spec.to_tool_list_async()
agent = FunctionAgent(
tools=tools,
llm=OpenAI(model="gpt-4o"),
system_prompt=(
"You are an assistant with access to Runway ML. "
"You have 10 tools available."
),
)
response = await agent.run(
"What tools are available in Runway ML?"
)
print(response)
asyncio.run(main())
* 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.
LlamaIndex agents combine Runway ML tool responses with indexed documents for comprehensive, grounded answers. Connect 10 tools through Vinkius and query live data alongside vector stores and SQL databases in a single turn. ideal for hybrid search, data enrichment, and analytical workflows.
What you can do
- Text-to-Video Generation — Write detailed prompts to synthesize entirely new, imaginative scenes using
gen3_turbo,gen4_turbo, or the standardtext_to_videotooling. - Image-to-Video Animation — Bring still images to life using
image_to_videoor precisely guide the motion of a starting image with a textual director prompt usingimage_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 LlamaIndex 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 LlamaIndex via MCP
Follow these steps to integrate the Runway ML MCP Server with LlamaIndex.
Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai
Replace the token
Replace [YOUR_TOKEN_HERE] with your Vinkius token
Run the agent
Save to agent.py and run: python agent.py
Explore tools
The agent discovers 10 tools from Runway ML
Why Use LlamaIndex with the Runway ML MCP Server
LlamaIndex provides unique advantages when paired with Runway ML through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine Runway ML tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain Runway ML tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query Runway ML, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what Runway ML tools were called, what data was returned, and how it influenced the final answer
Runway ML + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the Runway ML MCP Server delivers measurable value.
Hybrid search: combine Runway ML real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query Runway ML to augment indexed data with live information before generating user-facing responses
Knowledge base agents: build agents that maintain and update knowledge bases by periodically querying Runway ML for fresh data
Analytical workflows: chain Runway ML queries with LlamaIndex's data connectors to build multi-source analytical reports
Runway ML MCP Tools for LlamaIndex (10)
These 10 tools become available when you connect Runway ML to LlamaIndex 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 LlamaIndex
Ready-to-use prompts you can give your LlamaIndex 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 LlamaIndex
Common issues when connecting Runway ML to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpRunway ML + LlamaIndex FAQ
Common questions about integrating Runway ML MCP Server with LlamaIndex.
How does LlamaIndex connect to MCP servers?
Can I combine MCP tools with vector stores?
Does LlamaIndex support async MCP calls?
Connect Runway ML with your favorite client
Step-by-step setup guides for every MCP-compatible client and framework:
Anthropic's native desktop app for Claude with built-in MCP support.
AI-first code editor with integrated LLM-powered coding assistance.
GitHub Copilot in VS Code with Agent mode and MCP support.
Purpose-built IDE for agentic AI coding workflows.
Autonomous AI coding agent that runs inside VS Code.
Anthropic's agentic CLI for terminal-first development.
Python SDK for building production-grade OpenAI agent workflows.
Google's framework for building production AI agents.
Type-safe agent development for Python with first-class MCP support.
TypeScript toolkit for building AI-powered web applications.
TypeScript-native agent framework for modern web stacks.
Python framework for orchestrating collaborative AI agent crews.
Leading Python framework for composable LLM applications.
Data-aware AI agent framework for structured and unstructured sources.
Microsoft's framework for multi-agent collaborative conversations.
Connect Runway ML to LlamaIndex
Get your token, paste the configuration, and start using 10 tools in under 2 minutes. No API key management needed.