How to Use the Feathery MCP in Pydantic AI

Build type-safe Feathery automations with Pydantic AI for strict runtime validation.

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Connect Feathery MCP to Pydantic AI

Create your Vinkius account to connect Feathery 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 Feathery with Pydantic AI

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Type-safe Feathery tools for Pydantic AI

Every response from this MCP Server is validated against your Pydantic models. Use `get_form_details` and trust the schema your agent receives. No more silent corruption or hallucinated fields. If the Feathery API returns unexpected data, your agent stops immediately.

Deep integration with Pydantic AI models

Your agent can use `get_form_session` to retrieve state and `get_user_data` to pull field values. The agent works with any model you prefer. It keeps your logic clean. You define the expected output, and Pydantic AI handles the validation of every tool response.

Monitor Feathery connectors in Pydantic AI

Use `list_connector_logs` to debug your integrations. Pydantic AI ensures the log data matches your expected format before the agent processes it. It’s a reliable way to handle error reporting. Your agent only acts on valid, verified log information.

Setup guide

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

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

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

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Why Choose Vinkius

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Real-time monitoring

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Connect your favorite tools to your AI and see exactly what's happening — every request, every response, in real time.

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place for every integration

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Common questions about Feathery MCP in Pydantic AI

Add the mcp-slim package to your environment. Initialize the MCPToolset with your server URL and pass it to your Agent definition.

It guarantees that the data your agent processes matches your schema. You get immediate validation errors instead of runtime crashes.

Yes. The toolset supports both Streamable HTTP and SSE transports, giving you flexibility in how your agent connects to the server.

Yes. Since you define your models, you can handle missing fields or API errors gracefully within your agent's execution flow.

The server enforces strict data typing on all retrieved form submissions. Your agent only processes the fields defined in your models, ensuring consistent access to user data.

Use it with your favorite AI tools

Connect this server to Cursor, Claude, VS Code, and more.

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