How to Use the Elai AI Video MCP in Pydantic AI

Q: Can Pydantic AI validate my Elai AI Video project lists?

Yes. When you call listaivideos, the agent validates the list structure against your defined Pydantic types. This prevents unexpected schema changes from breaking your code.

Q: How does Pydantic AI handle credit checking for Elai AI Video?

It calls getelaiaccountmetadata and validates the response against your budget models. Your agent will fail if the balance data does not match your schema.

Type-safe video generation in Pydantic AI. Validate your Elai AI Video workflows with runtime schema enforcement.

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Connect Elai AI Video MCP to Pydantic AI

Create your Vinkius account to connect Elai AI Video to Pydantic AI and route execution through our secure gateway. The platform manages server hosting, runtime updates, and security layers. Configuration requires no manual server provisioning.

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Setup Elai AI Video with Pydantic AI

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Perplexity

Type-safe video creation for Pydantic AI

Every response from `create_new_ai_video` is checked against your Pydantic models. If the API returns malformed data, the agent halts immediately. This ensures your video production remains error-free. You gain confidence in every automated pipeline step.

Validate assets in Pydantic AI

Your agent fetches valid voices using `list_available_voices` and matches them to your models. It ensures the inputs for `trigger_video_rendering` are always correct. No silent failures occur during your build. You maintain rigorous control over your creative outputs.

Strict monitor for Pydantic AI agents

Use `get_video_details` to verify the state of your projects. The agent validates the returned status against your strict schema requirements. It provides a predictable interface for your agent. You know exactly what data you are getting back at every stage.

Setup guide

Set up Elai AI Video 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": {
        "elai-ai-video-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

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

result = await agent.run("List recent Elai AI Video transactions")
print(result.output)

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Common questions about Elai AI Video MCP in Pydantic AI

Install the mcp-slim package and add the server to your toolsets. Your Pydantic models will automatically enforce the structure of the tool responses.

Yes. When you call `list_ai_videos`, the agent validates the list structure against your defined Pydantic types. This prevents unexpected schema changes from breaking your code.

The server provides the structure, but you can define models to constrain the data further. Use these to ensure your agent only interacts with specific video templates.

It calls `get_elai_account_metadata` and validates the response against your budget models. Your agent will fail if the balance data does not match your schema.

All communication is authenticated via your unique endpoint token. Your metadata, including video titles and IDs, is handled in memory and never persisted.

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