How to Use the YouTube MCP in Pydantic AI

Q: How do I use YouTube analytics with Pydantic AI?

You let your agent call getchannel. Because the response is validated by Pydantic, you know exactly which fields you're working with and that they follow a predictable structure.

Q: Can I search for YouTube videos with Pydantic AI?

You use searchvideos. The framework validates the returned metadata list. If the data is malformed, your agent fails gracefully with an explicit error message, not a vague runtime exception.

Q: What happens if I need comment data with Pydantic AI?

You call listcomments. The framework validates that the returned comment threads match the expected structure. This prevents your agent from misinterpreting incomplete or oddly formatted comments.

Q: Does this MCP Server support YouTube data types with Pydantic AI?

Yes, it handles core public statistics: channel details (getchannel), video metadata (getvideo), and comment threads. All tools adhere to strict runtime type validation.

Build type-safe YouTube analytics agents with Pydantic AI.

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Connect YouTube MCP to Pydantic AI

Create your Vinkius account to connect YouTube 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 YouTube with Pydantic AI

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Audit Channel Performance

Start by using `get_channel` to get all the stats and branding information for a YouTube channel. The response is validated immediately against your defined models, ensuring you get clean data every time. This makes building reliable agents much easier because if the API sends unexpected fields, your agent fails loud—it doesn't just silently break.

Search for Video Content

Need to find videos? `search_videos` returns a list of titles and descriptions. The response is validated against Pydantic models before it hits your agent logic. This means you can trust the structure of the data when iterating through search results, which is crucial for correctness.

Extract Video Details

The `get_video` tool retrieves full metadata and stats for a specific YouTube video. Every field—from the description to the view count—is strictly typed and validated on return. This focus on correctness means your agent handles complex data structures without fear of corrupted or unexpected fields.

Setup guide

Set up YouTube 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": {
        "youtube-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

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

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

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Common questions about YouTube MCP in Pydantic AI

You let your agent call `get_channel`. Because the response is validated by Pydantic, you know exactly which fields you're working with and that they follow a predictable structure.

You use `search_videos`. The framework validates the returned metadata list. If the data is malformed, your agent fails gracefully with an explicit error message, not a vague runtime exception.

You call `list_comments`. The framework validates that the returned comment threads match the expected structure. This prevents your agent from misinterpreting incomplete or oddly formatted comments.

Yes, it handles core public statistics: channel details (`get_channel`), video metadata (`get_video`), and comment threads. All tools adhere to strict runtime type validation.

The server touches YouTube public metadata, including channel statistics, video titles/descriptions, and user-generated comment content. Every piece of returned data is validated for type safety.

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