How to Use the Mux MCP in Pydantic AI

Q: Does Pydantic AI validate Mux asset listings?

Yes, every call to listassets is checked against your schema. This ensures your agent never hallucinates or misinterprets video data.

Q: Can I create a live stream using Mux and Pydantic AI?

Yes, use createlivestream within your agent. Pydantic AI will verify the returned stream object before your code interacts with it.

Type-safe Mux video management for Pydantic AI. Validate every asset and stream action with strict runtime schemas.

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Connect Mux MCP to Pydantic AI

Create your Vinkius account to connect Mux 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 Mux with Pydantic AI

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Validated asset creation in Pydantic AI

Invoke `create_asset` and have the response validated against your Pydantic models. Pydantic AI catches any unexpected API fields before your agent logic processes them. List your video inventory with `list_assets` to ensure your agent state matches the remote library. It forces the agent to respect the data structure returned by Mux.

Live stream operations with strict types

Initialize your broadcast sessions using `create_live_stream` with full schema enforcement. Pydantic AI ensures your agent receives only the expected stream key format. Check stream health using `get_live_stream` and validate the output. If the Mux API changes, your agent will fail with a clear validation error rather than corrupting your state.

Analytics with runtime safety

Retrieve metrics using `get_recent_views` and map them directly to your data classes. Pydantic AI guarantees your agent only acts on confirmed numbers. Manage your storage by calling `delete_asset` when your logic confirms a file is redundant. It maintains the integrity of your Mux account based on your defined rules.

Setup guide

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

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

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

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Common questions about Mux MCP in Pydantic AI

You define an MCPToolset with your Vinkius server URL and pass it to your Agent. Pydantic AI then validates all incoming tool responses against your models.

Yes, every call to `list_assets` is checked against your schema. This ensures your agent never hallucinates or misinterprets video data.

Yes, use `create_live_stream` within your agent. Pydantic AI will verify the returned stream object before your code interacts with it.

Pydantic AI raises a validation error immediately. This stops the agent from continuing with malformed data.

Your data is protected by strict type enforcement and token-based authentication. Your agent only accesses the resources authorized by your endpoint key.

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