How do I obtain my Feathery API Key?

Log in to your Feathery dashboard, navigate to **Settings > API**, and generate a new key. Ensure you copy the token immediately as it is only shown once.

Can I see the data submitted by a specific user?

Yes! Use the `get_user_data` tool with the specific User ID to retrieve all field values submitted by that user across any of your forms.

How can I troubleshoot integration errors via chat?

The `list_connector_logs` tool retrieves recent error logs for your API connectors. This allows your agent to identify exactly which field mapping or endpoint caused a failure.

Feathery MCP Server for Pydantic AI 11 tools — connect in under 2 minutes

Name: Feathery
Availability: InStock
Author: Vinkius

ai.pydantic.dev/mcp

Built by Vinkius GDPR 11 Tools SDK

Pydantic AI brings type-safe agent development to Python with first-class MCP support. Connect Feathery through the Vinkius and every tool is automatically validated against Pydantic schemas — catch errors at build time, not in production.

Get MCP Server for AI Agents

ASK AI ABOUT THIS MCP SERVER

Open in ChatGPT Open in Claude Open in Perplexity

Vinkius supports streamable HTTP and SSE.

python

import asyncio
from pydantic_ai import Agent
from pydantic_ai.mcp import MCPServerHTTP

async def main():
    # Your Vinkius token — get it at cloud.vinkius.com
    server = MCPServerHTTP(url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")

    agent = Agent(
        model="openai:gpt-4o",
        mcp_servers=[server],
        system_prompt=(
            "You are an assistant with access to Feathery "
            "(11 tools)."
        ),
    )

    result = await agent.run(
        "What tools are available in Feathery?"
    )
    print(result.data)

asyncio.run(main())

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Stream every event to Splunk, Datadog, or your own webhook in real-time

* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure

About Feathery MCP Server

Connect your Feathery.io account to any AI agent and take full control of your form automation and user data management through natural conversation.

Pydantic AI validates every Feathery tool response against typed schemas, catching data inconsistencies at build time. Connect 11 tools through the Vinkius and switch between OpenAI, Anthropic, or Gemini without changing your integration code — full type safety, structured output guarantees, and dependency injection for testable agents.

What you can do

User Orchestration — List all users in your environment and fetch detailed profiles including submission history natively
Submission Intelligence — Retrieve granular field data submitted by specific users across all your automated forms flawlessly
Session Monitoring — Query current form sessions to understand user progress and friction points in real-time
Connector Auditing — List API connector logs to verify data synchronization and troubleshoot integration errors synchronously
Form Management — List all active forms and retrieve structural details and metadata directly from the cloud
Workflow Tracking — Inspect automated workflows and their execution status to ensure seamless user journeys
Identity Context — Verify your API token user profile and account information through the agent flawlessly

The Feathery MCP Server exposes 11 tools through the Vinkius. Connect it to Pydantic AI in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.

How to Connect Feathery to Pydantic AI via MCP

Follow these steps to integrate the Feathery MCP Server with Pydantic AI.

Install Pydantic AI

Run pip install pydantic-ai

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token

Run the agent

Save to agent.py and run: python agent.py

Explore tools

The agent discovers 11 tools from Feathery with type-safe schemas

Why Use Pydantic AI with the Feathery MCP Server

Pydantic AI provides unique advantages when paired with Feathery through the Model Context Protocol.

Full type safety: every MCP tool response is validated against Pydantic models, catching data inconsistencies before they reach your application

Model-agnostic architecture — switch between OpenAI, Anthropic, or Gemini without changing your Feathery integration code

Structured output guarantee: Pydantic AI ensures tool results conform to defined schemas, eliminating runtime type errors

Dependency injection system cleanly separates your Feathery connection logic from agent behavior for testable, maintainable code

Feathery + Pydantic AI Use Cases

Practical scenarios where Pydantic AI combined with the Feathery MCP Server delivers measurable value.

Type-safe data pipelines: query Feathery with guaranteed response schemas, feeding validated data into downstream processing

API orchestration: chain multiple Feathery tool calls with Pydantic validation at each step to ensure data integrity end-to-end

Production monitoring: build validated alert agents that query Feathery and output structured, schema-compliant notifications

Testing and QA: use Pydantic AI's dependency injection to mock Feathery responses and write comprehensive agent tests

Feathery MCP Tools for Pydantic AI (11)

These 11 tools become available when you connect Feathery to Pydantic AI via MCP:

get_account_info

Get Feathery account details

get_form_details

Get details for a specific form

get_form_session

Retrieve the current state/session of a specific form for a user

get_me

Get current API token identity info

get_user_data

Get all field values submitted by a specific user across forms

get_workflow_details

Get details for a specific workflow

list_connector_logs

List recent API connector error logs for a specific form

list_environments

List available Feathery environments

list_forms

List all forms in your Feathery account

list_users

List all users in your Feathery environment

list_workflows

List all automated workflows

Example Prompts for Feathery in Pydantic AI

Ready-to-use prompts you can give your Pydantic AI agent to start working with Feathery immediately.

"List all active forms in my account."

"Show me the data submitted by user user_99."

"Check if there are any connector errors for the Onboarding form."

Troubleshooting Feathery MCP Server with Pydantic AI

Common issues when connecting Feathery to Pydantic AI through the Vinkius, and how to resolve them.

MCPServerHTTP not found

Update: pip install --upgrade pydantic-ai

Feathery + Pydantic AI FAQ

Common questions about integrating Feathery MCP Server with Pydantic AI.

How does Pydantic AI discover MCP tools?

Create an MCPServerHTTP instance with the server URL. Pydantic AI connects, discovers all tools, and generates typed Python interfaces automatically.

Does Pydantic AI validate MCP tool responses?

Yes. When you define result types as Pydantic models, every tool response is validated against the schema. Invalid data raises a clear error instead of silently corrupting your pipeline.

Can I switch LLM providers without changing MCP code?

Absolutely. Pydantic AI abstracts the model layer — your Feathery MCP integration works identically with OpenAI, Anthropic, Google, or any supported provider.