How to Use the Twelve Labs (Video Understanding) MCP in Pydantic AI

Ensure perfect video data structure validation with Pydantic AI.

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Connect Twelve Labs (Video Understanding) MCP to Pydantic AI

Create your Vinkius account to connect Twelve Labs (Video Understanding) 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 Twelve Labs (Video Understanding) with Pydantic AI

Ask AI about this MCP

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Claude

Perplexity

Structured extraction from videos

When your agent runs `search`, it doesn't just get a list of keywords; the response is validated against your schema. This means you get predictable, trustworthy fields for every search result. The framework enforces that when the server returns data—say, an entity name or location—it matches the exact Python type defined in your Pydantic model. No garbage output.

Managing video content pipelines

The agent can manage the entire asset lifecycle using `create_multipart_upload` and then confirm it with `confirm_multipart_upload`. Crucially, if any step returns malformed data, the Pydantic validation layer fails loudly before your code runs. This prevents silent corruption. You know immediately when the video upload process has hit a roadblock or returned unexpected payload structure.

Creating verifiable knowledge graphs

To build a reliable profile, you call `create_entity` and define its required fields upfront. The agent can then use `get_index` to pull metadata, which is immediately validated against your model. If the video understanding server returns an asset ID that doesn't fit the expected integer type, the Pydantic framework throws a clear error, protecting your downstream code.

Setup guide

Set up Twelve Labs (Video Understanding) 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": {
        "twelve-labs-video-understanding-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

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

result = await agent.run("List recent Twelve Labs (Video Understanding) transactions")
print(result.output)

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Common questions about Twelve Labs (Video Understanding) MCP in Pydantic AI

The primary benefit is validation. It forces the output of tools like `analyze_sync` into a strictly typed Python object. You never have to guess what data fields you received; they're guaranteed by your schema.

The server touches video content, which is uploaded as an asset. However, the agent interacts with and validates structured metadata—things like entity collections or index records—using specific Python models.

Yes. Because everything is typed, you can define nested schemas. For example, an `entity` could contain a list of associated `locations`, and the framework validates every single level of that structure.

It covers all aspects of video analysis, but with an added layer of runtime rigor. You can use `search` and `analyze_async`, knowing that the data returned for every successful query will match your defined structure.

It is its core function. It guarantees runtime correctness. If any tool—whether `list_indexes` or `get_index`—returns a value that violates your defined types, the agent fails safely and loudly.

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