How to Use the Vimeo MCP in Pydantic AI

Q: How do I search for Vimeo content using Pydantic AI?

The agent uses searchvideos, passing your query. The critical part is that Pydantic validates the resulting list against a predefined model, so you know exactly what structure to expect from Vimeo.

Q: Can I delete videos from my Vimeo account using Pydantic AI?

You invoke deletevideo through your agent. The tool's output is validated immediately by the framework, confirming the success or failure of the deletion action.

Q: What happens when listing groups on Vimeo with Pydantic AI?

When you run listgroups, the raw data from Vimeo passes through a Pydantic model. This guarantees that even if Vimeo changes its API response slightly, your agent won't break because of unexpected fields.

Q: What is the process for updating video metadata using Pydantic AI?

The system uses updatevideo. Before execution, the data you provide (like a new title) must validate against your Pydantic schema. This prevents incorrect or malformed updates from reaching Vimeo.

Guarantee data correctness for Vimeo with Pydantic AI.

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Connect Vimeo MCP to Pydantic AI

Create your Vinkius account to connect Vimeo 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 Vimeo with Pydantic AI

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Verify Video Information and Status

To get video details, the agent calls `get_video`, which returns a structured object that Pydantic validates instantly. If Vimeo sends unexpected fields, your system fails immediately, not silently. Similarly, when you use `list_videos` to get an inventory, every resulting item is validated against your schema.

Map Out Your Entire Content Ecosystem

The agent uses tools like `list_folders`, `list_showcases`, and `list_channels` to gather content structure. Each tool's output is parsed through a Pydantic model, meaning you can trust the format of your project map. This validation process prevents runtime errors when combining data from various Vimeo sources.

Search and Modify Content Reliably

You run `search_videos` to find public content, but Pydantic ensures the search results always match your expected format. If you need to change details using `update_video`, the input must validate perfectly. If a video needs removal, calling `delete_video` confirms the action and records the result in a structured way.

Setup guide

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

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

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

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Common questions about Vimeo MCP in Pydantic AI

The agent uses `search_videos`, passing your query. The critical part is that Pydantic validates the resulting list against a predefined model, so you know exactly what structure to expect from Vimeo.

You invoke `delete_video` through your agent. The tool's output is validated immediately by the framework, confirming the success or failure of the deletion action.

When you run `list_groups`, the raw data from Vimeo passes through a Pydantic model. This guarantees that even if Vimeo changes its API response slightly, your agent won't break because of unexpected fields.

The system uses `update_video`. Before execution, the data you provide (like a new title) must validate against your Pydantic schema. This prevents incorrect or malformed updates from reaching Vimeo.

This MCP Server touches video metadata and user profiles via `get_me`. By enforcing strict type-checking on all returned data, your agent ensures that only validated, correct records are used for subsequent actions.

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