How to Use the Dailymotion MCP in Pydantic AI

Q: How does Pydantic AI validate Dailymotion API responses?

The framework wraps the getvideo or listvideos tool outputs in Pydantic models. Every field, from view counts to stream URLs, is checked against your defined types at runtime.

Q: Can I use Pydantic AI to safely delete Dailymotion videos?

Yes. You expose deletevideo to your agent, which requires a validated string ID. The framework ensures the agent passes a properly formatted video ID before executing the deletion.

Q: How does this MCP Server handle the security of my Dailymotion account details and API tokens?

Your account details fetched by getuser and your API tokens are processed entirely in memory within your local Python runtime and secure Vinkius sandbox. No sensitive data is logged or shared with third-party model providers.

Validate Dailymotion video metadata at runtime using the type-safe Pydantic AI framework.

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Connect Dailymotion MCP to Pydantic AI

Create your Vinkius account to connect Dailymotion 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 Dailymotion with Pydantic AI

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Type-safe metadata writes with Pydantic AI

The MCP Server tool `update_video` modifies Dailymotion video metadata while enforcing strict type-checking on your title, tags, and description fields. The Pydantic AI framework ensures that the arguments generated by your agent match your exact Dailymotion schema before hitting the API. If your agent attempts to pass an invalid Dailymotion channel ID or malformed tag array, the framework blocks the execution instantly. This prevents API errors and keeps your Dailymotion channel organization clean.

Schema-guaranteed playlist parsing via MCP Server

This MCP toolset lets `list_playlists` return your Dailymotion playlists and video counts directly into typed Python models. Your Pydantic AI agent reads this structured data knowing every playlist ID and integer count is validated at runtime. This validation eliminates silent failures where the LLM might misinterpret a null value or stringified number in your Dailymotion playlist. You can confidently build automated curation logic in Pydantic AI knowing the Dailymotion input types are guaranteed.

Safe video detail retrieval

`get_video` retrieves detailed metadata for a specific Dailymotion video, including stream URLs, view counts, and tags. The Pydantic AI framework parses this Dailymotion payload into a strict model, stripping out unexpected fields. This guarantees your downstream application code receives clean, typed Dailymotion data. If the Dailymotion API changes its response format, your Pydantic AI system raises an explicit validation error instead of processing corrupt data.

Setup guide

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

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

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

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Common questions about Dailymotion MCP in Pydantic AI

The framework wraps the `get_video` or `list_videos` tool outputs in Pydantic models. Every field, from view counts to stream URLs, is checked against your defined types at runtime.

The framework raises a validation error immediately. Instead of passing corrupt video metadata to your agent, it stops execution so you can handle the schema mismatch safely.

Yes. You expose `delete_video` to your agent, which requires a validated string ID. The framework ensures the agent passes a properly formatted video ID before executing the deletion.

You initialize a unified toolset pointing to the server's HTTP endpoint. The framework automatically discovers tools like `publish_video` and registers them with your agent.

Your account details fetched by `get_user` and your API tokens are processed entirely in memory within your local Python runtime and secure Vinkius sandbox. No sensitive data is logged or shared with third-party model providers.

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