How to Use the Luma AI (Generative Video & Creative) MCP in Pydantic AI

Q: How do I set up Luma AI (Generative Video & Creative) with Pydantic AI?

Use pip install "pydantic-ai-slimmcp" and initialize the MCPToolset with your server URL. Add it to your agent's toolsets list to get started.

Q: Does Luma AI (Generative Video & Creative) support Pydantic AI validation for all tools?

Yes. Every tool from the server, including lm.cameracontrol and lm.interpolate, is automatically wrapped and validated by the Pydantic framework.

Build type-safe creative agents with Luma AI video tools and Pydantic AI validation.

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Setup Luma AI (Generative Video & Creative) with Pydantic AI

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Perplexity

Type-safe Luma AI tool execution

Every response from the Luma MCP Server is validated against Pydantic models at runtime. If `lm.get_generation` returns malformed data, your agent catches it immediately. This prevents silent failures during your creative pipeline. You get the reliability of strict schemas while using the power of the Dream Machine API.

Robust error handling for video tasks

Because Pydantic AI enforces type safety, your agent logic remains clean and predictable. If a video generation request misses a parameter, the validation layer blocks the call before it hits the API. This reduces your error rate when chaining tools like `lm.text_to_image` and `lm.image_to_video`. You define the interface and the framework ensures compliance.

Unified toolset for Pydantic AI

Register the Luma MCP Server using the `MCPToolset` to bring all ten generation and control tools into your agent. It integrates with your existing Pydantic-based workflows without extra configuration. Your agent can now use these tools as if they were local functions. The type hints allow for autocompletion and static analysis of your creative agent's code.

Setup guide

Set up Luma AI (Generative Video & Creative) MCP in Pydantic AI

Prerequisites

Python 3.10+ installed
pydantic-ai-slim[fastmcp] package
Active Vinkius subscription with a valid endpoint token

1

Install Pydantic AI with FastMCP
Run pip install "pydantic-ai-slim[fastmcp]". The FastMCP toolset replaces the deprecated MCPServerHTTP class with full protocol support.
2

Configure the FastMCPToolset
Pass a JSON-style config dict to FastMCPToolset with your Vinkius URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. Supports Streamable HTTP, SSE, and Stdio transports.
3

Create and run your agent
Pass the toolset to Agent(toolsets=[toolset]) and call agent.run(). Swap openai:gpt-4o for any supported model — Anthropic, Google, Mistral, or Groq.

agent.py

from pydantic_ai import Agent
from pydantic_ai.toolsets.fastmcp import FastMCPToolset

toolset = FastMCPToolset({
    "mcpServers": {
        "luma-ai-generative-video-creative-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

agent = Agent(
    "openai:gpt-4o",
    toolsets=[toolset],
    system_prompt="You have access to Luma AI (Generative Video & Creative) tools.",
)

result = await agent.run("List recent Luma AI (Generative Video & Creative) transactions")
print(result.output)

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Common questions about Luma AI (Generative Video & Creative) MCP in Pydantic AI

It provides runtime validation for every API response. This ensures your agent never processes corrupt video URLs or incomplete generation states.

Use `pip install "pydantic-ai-slim[mcp]"` and initialize the `MCPToolset` with your server URL. Add it to your agent's toolsets list to get started.

Yes. Every tool from the server, including `lm.camera_control` and `lm.interpolate`, is automatically wrapped and validated by the Pydantic framework.

Absolutely. Since Pydantic AI is model-agnostic, you can swap between OpenAI, Anthropic, or local models without changing your Luma server integration.

Data is validated locally before transmission. No sensitive logs or generation content are stored on the server side, ensuring your creative pipeline remains isolated.

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