How to Use the Vadoo AI MCP in Pydantic AI

Q: What data types can Vadoo AI handle for podcast generation with Pydantic AI?

The vadooaigeneratepodcast tool accepts standard text prompts. Crucially, the output job status is validated against a defined model, giving you confidence in the returned data structure.

Q: Can I use Vadoo AI to generate multiple video assets with Pydantic AI?

Yes. You can chain vadooaigeneratevideo and then reliably run vadooaicreateclips. The validation layer ensures that the metadata from the first step correctly feeds into the second.

Q: How do I check if a Vadoo AI job finished using Pydantic AI?

You call vadooaicheckgenerationstatus. Because of the validation layer, you know that if this function returns a successful status code, the data fields themselves are correctly typed and structured.

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Connect Vadoo AI MCP to Pydantic AI

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Setup Vadoo AI with Pydantic AI

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Generate videos, ensuring predictable outputs.

When you run `vadooai_generate_video`, the agent expects a specific output format—the job ID and status. If Vadoo AI deviates from that structure, your agent fails loudly with a validation error. This type-safe approach guarantees that even if the API changes, your calling code remains correct because Pydantic catches it upfront.

Structure video metadata for reliable processing.

`vadooai_create_clips` returns a list of clips. With Pydantic AI, you're guaranteed that every clip entry has the exact fields (start time, end time, etc.) your code expects. This removes ambiguity and prevents silent data corruption when passing video details downstream to other services.

Validate podcast or caption results instantly.

After calling `vadooai_generate_podcast` or `vadooai_generate_caption`, the agent validates the returned captions and metadata against a strict schema. You don't have to worry about missing fields. Correctness is guaranteed. If Vadoo AI sends back garbage, your program stops, pointing directly to the bad data.

Setup guide

Set up Vadoo AI 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": {
        "vadoo-ai-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

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

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

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Common questions about Vadoo AI MCP in Pydantic AI

The `vadooai_generate_podcast` tool accepts standard text prompts. Crucially, the output job status is validated against a defined model, giving you confidence in the returned data structure.

Yes. You can chain `vadooai_generate_video` and then reliably run `vadooai_create_clips`. The validation layer ensures that the metadata from the first step correctly feeds into the second.

You call `vadooai_check_generation_status`. Because of the validation layer, you know that if this function returns a successful status code, the data fields themselves are correctly typed and structured.

It's excellent. The type safety makes complex, multi-stage pipelines reliable. You eliminate entire classes of bugs related to unexpected API responses and data mismatches.

This server touches video and text generation data. The primary benefit is that the structured nature of the output makes it easier for your agent to log, audit, and manage compliance related to that content.

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