How to Use the Fairing MCP in Pydantic AI

Build type-safe customer insight agents using Fairing and Pydantic AI for complete runtime validation.

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Connect Fairing MCP to Pydantic AI

Create your Vinkius account to connect Fairing 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 Fairing with Pydantic AI

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Validate Fairing data with Pydantic AI

Every response from `get_insights` is checked against your Pydantic models at runtime. If the API returns a malformed field, your agent fails safely instead of proceeding with bad data. This ensures your application logic remains predictable and stable. You define the structure, and the MCP server maps the incoming survey data to your schema.

Query survey responses with Pydantic AI

Your agent uses `list_responses` to retrieve batches of customer feedback. Pydantic AI enforces strict typing on every object, ensuring your downstream analysis doesn't crash on unexpected inputs. It handles the conversion from API raw data to your custom Python objects. This workflow allows you to build complex agent logic without worrying about silent data corruption.

Access account metadata safely

The `get_account_info` tool provides the necessary context for your agent to verify its current environment. It returns a structured object that Pydantic AI immediately validates. This keeps your agent informed about its own limits and configurations. Using `get_me` adds another layer of identity verification before the agent executes any data-intensive tasks.

Setup guide

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

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

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

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Common questions about Fairing MCP in Pydantic AI

Because the tools are validated against your Pydantic models, any unexpected schema change triggers a clear validation error. This prevents your agent from hallucinating fields that no longer exist.

Communication occurs over secure, ephemeral channels hosted by Vinkius. Your Pydantic AI agent interacts with the server using an encrypted token, ensuring no data leakage.

Your Pydantic model definitions determine how nulls are handled. You can explicitly allow or reject them in your code, giving you full control over your agent's behavior.

Yes, all 12 tools are available for use within your agent's toolsets. You can selectively enable them to keep your agent's decision space focused and efficient.

The server processes only the survey responses and metadata you authorize. By using strict typing in Pydantic AI, you ensure that only the expected data points are handled by your agent.

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