How to Use the Videco MCP in Pydantic AI

Q: Can I check Videco status using Pydantic AI?

Yes. You can use the checkvidecostatus tool. This is a simple, reliable call that confirms connectivity without needing complex data validation afterwards.

Q: What data does the Videco MCP Server manage for analytics?

The server handles video performance metrics via getvideoanalytics. When these numbers come back, Pydantic validates that they are numeric types and adhere to the expected schema.

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Connect Videco MCP to Pydantic AI

Create your Vinkius account to connect Videco 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 Videco with Pydantic AI

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Video Content Management

Your agent uses `list_videos` to pull every video record, and it gets specifics via the `get_video` tool. The server also lets you track performance through `get_video_analytics`. If you need a new asset, call `create_video`. If the API response for video metadata is unexpected, Pydantic ensures your agent fails loudly with a validation error—no silent corruption.

Campaign Lifecycle Control

The system lets you list every campaign using `list_campaigns`, and you can retrieve deep details on any single one via `get_campaign`. This keeps your marketing efforts verifiable. To initiate a new effort, call `create_campaign`. If the required parameters for creation are missing or incorrect, Pydantic validates that failure before the request even leaves your agent.

Lead Data Integrity

The agent accesses lead data by calling `list_leads` and getting specifics with `get_lead`. This is critical when you need to guarantee data types for follow-up actions. When the API returns a lead record, Pydantic validates that every field—like email or name—is present and correctly typed. You don't risk hallucinated fields.

Setup guide

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

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

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

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Common questions about Videco MCP in Pydantic AI

The MCP Server exposes all tools, but Pydantic adds a crucial layer of runtime type safety. Every time your agent calls `get_campaign` or `list_leads`, the response is checked against defined Python models first.

The server manages structured contact and campaign attribution details. Because of Pydantic, you'll always know exactly what fields are available when running `get_lead`, preventing bad downstream code.

Yes. You can use the `check_videco_status` tool. This is a simple, reliable call that confirms connectivity without needing complex data validation afterwards.

The system guarantees correctness over speed. If the backend returns bad JSON or missing fields when calling `list_videos`, your agent won't proceed; it will throw an explicit validation error.

The server handles video performance metrics via `get_video_analytics`. When these numbers come back, Pydantic validates that they are numeric types and adhere to the expected schema.

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