How to Use the Huddle01 (Web3 Video API) MCP in Pydantic AI

Q: How do I configure the Huddle01 (Web3 Video API) MCP Server in Pydantic AI?

Install "pydantic-ai-slimmcp". Initialize MCPToolset with your endpoint URL and pass it to your agent's toolsets array.

Q: Will Pydantic AI catch missing parameters when using the Huddle01 (Web3 Video API)?

Yes. If the model forgets to specify the lock status in createroom, the framework throws a validation error before executing the request.

Q: What happens if the Huddle01 (Web3 Video API) returns unexpected data to Pydantic AI?

The agent fails loudly. If getparticipantlist returns an array of objects instead of the expected strings, Pydantic AI blocks the execution to prevent silent corruption.

Enforce strict typing on Huddle01 Web3 video API calls with Pydantic AI.

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Setup Huddle01 (Web3 Video API) with Pydantic AI

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Perplexity

Type-safe Web3 room creation

The `create_room` tool requires your agent to provision Huddle01 meetings with a mandatory lock status boolean. Pydantic AI validates this parameter at runtime. It fails loudly if the model skips the requirement. You get a guaranteed schema match before the API call ever hits the network. You can swap between a local model and Anthropic without changing code. The framework guarantees your room configurations remain strictly typed.

Validate live session telemetry

Your agent monitors ongoing calls using the `get_live_session_details` and `get_live_session_participants` MCP tools. Every participant ID and session metric returned from the server gets checked against your Pydantic models. If the API returns an unexpected data type for a participant's wallet address, the agent throws a validation error immediately. You never have to deal with silent data corruption in your backend systems.

Query recordings via Pydantic AI MCP Server

The `get_recordings` tool pulls meeting media assets, filtering strictly by the provided session ID. The agent also retrieves complete room histories via `get_room_sessions`, mapping the output directly to your defined schemas. You can monitor your key's usage statistics by calling `get_metrics`. The framework guarantees that the usage numbers arrive as integers, rejecting hallucinated string values from the underlying language model.

Setup guide

Set up Huddle01 (Web3 Video API) 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": {
        "huddle01-web3-video-api-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

agent = Agent(
    "openai:gpt-4o",
    toolsets=[toolset],
    system_prompt="You have access to Huddle01 (Web3 Video API) tools.",
)

result = await agent.run("List recent Huddle01 (Web3 Video API) transactions")
print(result.output)

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Common questions about Huddle01 (Web3 Video API) MCP in Pydantic AI

Install `"pydantic-ai-slim[mcp]"`. Initialize `MCPToolset` with your endpoint URL and pass it to your agent's `toolsets` array.

Yes. If the model forgets to specify the lock status in `create_room`, the framework throws a validation error before executing the request.

The agent fails loudly. If `get_participant_list` returns an array of objects instead of the expected strings, Pydantic AI blocks the execution to prevent silent corruption.

Yes. The framework is model-agnostic. You can connect a local Llama instance to this MCP integration and it will enforce the same strict typing rules.

The server fetches room configurations, recording URLs, and active session counts. Pydantic AI validates every piece of this video telemetry in memory, ensuring no malformed data enters your application state.

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