How to Use the FanPower (PickUp) MCP in Pydantic AI

Use Pydantic AI with the FanPower (PickUp) MCP Server for type-safe, validated engagement logic.

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Connect FanPower (PickUp) MCP to Pydantic AI

Create your Vinkius account to connect FanPower (PickUp) 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 FanPower (PickUp) with Pydantic AI

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Validate fan picks with Pydantic AI

Use `list_picks` and `get_pick_details` to pull fan data into your agent. Pydantic AI validates every field against your models, catching errors before they reach your logic. This ensures your agent never processes malformed data. You get immediate feedback if the API structure changes, keeping your application stable.

Strict question management with Pydantic AI

Invoke `list_questions` to fetch active engagement items. The framework enforces strict types on the returned questions, so your agent always knows the expected data format. Use `get_question` to drill down into specific details. You define the schema, and the agent ensures the incoming data matches your requirements exactly.

Type-safe reward tracking via Pydantic AI

Access `list_rewards` and `get_reward_details` with full type safety. Your agent checks reward availability against your defined models, preventing runtime crashes. This setup allows you to build complex reward logic with confidence. If an unexpected value appears, the agent halts, preventing invalid operations on your user accounts.

Setup guide

Set up FanPower (PickUp) 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": {
        "fanpower-pickup-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

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

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

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Common questions about FanPower (PickUp) MCP in Pydantic AI

It provides runtime validation for all tool responses. You define the expected data structure, and the framework rejects any output that doesn't match.

The framework manages the HTTP connection. If the server goes down, the agent throws a clear exception that you can handle in your main logic loop.

Yes. By defining strict models for fan profiles and picks, you ensure that only the data you explicitly permit is processed by your agent code.

Yes, the framework is model-agnostic. You can change the underlying model, and your type-safe tool definitions will remain consistent.

Store your endpoint tokens in environment variables. The server uses these for authentication, keeping your secrets out of the agent code.

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