How to Use the Betterfly MCP in Pydantic AI

Q: Does Betterfly support the Pydantic AI unified toolset?

Yes, you use MCPToolset to connect. It is the standard way to bridge this MCP Server with your agent's logic, replacing the older, less stable methods.

Use Betterfly with Pydantic AI for type-safe wellness data handling and strict response validation.

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Connect Betterfly MCP to Pydantic AI

Create your Vinkius account to connect Betterfly 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 Betterfly with Pydantic AI

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Enforce strict types in Pydantic AI

Every call to `get_user_rewards` or `list_benefits` is validated against your Pydantic models. If the data structure doesn't match your expected schema, the agent stops immediately. This approach eliminates silent data corruption. Your agent knows exactly what it's getting, and if the API changes, you'll see a clear validation error right in your logs.

Run reliable wellness agents with Betterfly

You can trust the output of `list_partners` or `get_impact_stats` because Pydantic AI enforces type safety at runtime. It is the safest way to build agents that interact with critical HR data. Use `MCPToolset` to integrate the server into your agent flow. The framework handles the communication, while your models handle the logic, ensuring a robust and predictable outcome.

Validate Betterfly inputs with Pydantic AI

When you trigger `assign_reward`, the framework ensures all arguments are correct before the request is sent. It keeps your agent from making invalid calls to the Betterfly backend. This setup is model-agnostic, so you can swap your underlying LLM without changing your tool definitions. It keeps your project clean and focused on what really matters.

Setup guide

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

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

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

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Common questions about Betterfly MCP in Pydantic AI

You get runtime validation for every piece of data returned by the server. It prevents the agent from hallucinating fields or processing malformed wellness records.

Yes, you use `MCPToolset` to connect. It is the standard way to bridge this MCP Server with your agent's logic, replacing the older, less stable methods.

Since the framework is model-agnostic, you can use any model supported by Pydantic AI. The type validation works the same regardless of which LLM you choose.

Pydantic AI throws a validation error immediately. This stops the agent from continuing with bad data, which is much safer than letting the agent guess the values.

Vinkius manages all connections through an ephemeral, zero-trust sandbox. This ensures that sensitive user wellness details and rewards data are never exposed beyond the immediate scope of your agent's execution.

Use it with your favorite AI tools

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