How to integrate Feathery MCP with Pydantic AI

This guide walks you through connecting Feathery to Pydantic AI using the Composio tool router. By the end, you'll have a working Feathery agent that can list all forms created this month, get schema for user registration form, fill document template with client details through natural language commands. This guide will help you understand how to give your Pydantic AI agent real control over a Feathery account through Composio's Feathery MCP server. Before we dive in, let's take a quick look at the key ideas and tools involved.

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Feathery is an AI-powered platform for building dynamic data intake forms with advanced logic. It helps teams automate complex workflows and collect structured data with ease.

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Introduction

This guide walks you through connecting Feathery to Pydantic AI using the Composio tool router. By the end, you'll have a working Feathery agent that can list all forms created this month, get schema for user registration form, fill document template with client details through natural language commands.

This guide will help you understand how to give your Pydantic AI agent real control over a Feathery account through Composio's Feathery MCP server.

Before we dive in, let's take a quick look at the key ideas and tools involved.

Also integrate Feathery with

OpenAI Agents SDKClaude Agent SDKClaude CodeClaude CoworkCodexOpenClawHermesCLIGoogle ADKLangChainVercel AI SDKMastra AILlamaIndexCrewAI

TL;DR

Here's what you'll learn:
  • How to set up your Composio API key and User ID
  • How to create a Composio Tool Router session for Feathery
  • How to attach an MCP Server to a Pydantic AI agent
  • How to stream responses and maintain chat history
  • How to build a simple REPL-style chat interface to test your Feathery workflows

What is Pydantic AI?

Pydantic AI is a Python framework for building AI agents with strong typing and validation. It leverages Pydantic's data validation capabilities to create robust, type-safe AI applications.

Key features include:

  • Type Safety: Built on Pydantic for automatic data validation
  • MCP Support: Native support for Model Context Protocol servers
  • Streaming: Built-in support for streaming responses
  • Async First: Designed for async/await patterns

What is the Feathery MCP server, and what's possible with it?

The Feathery MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Feathery account. It provides structured and secure access to your forms and workflow data, so your agent can perform actions like creating hidden fields, retrieving form schemas, listing documents, and managing account settings on your behalf.

  • Form discovery and management: Let your agent list all existing forms, retrieve specific form schemas, or permanently remove forms as needed.
  • Document automation and signing: Automatically fill or sign document templates and track generated document envelopes for streamlined data processing.
  • Hidden field configuration: Create new hidden fields or list all hidden fields within your forms for advanced workflow logic and data capture.
  • Account and team management: Fetch detailed account info, update user roles and permissions, and manage your Feathery team's access seamlessly.
  • Integration troubleshooting: List recent API connector errors tied to specific forms, making it easier to debug and maintain your integrations.

What is the Composio tool router, and how does it fit here?

What is Composio SDK?

Composio's Composio SDK helps agents find the right tools for a task at runtime. You can plug in multiple toolkits (like Gmail, HubSpot, and GitHub), and the agent will identify the relevant app and action to complete multi-step workflows. This can reduce token usage and improve the reliability of tool calls. Read more here: Getting started with Composio SDK

The tool router generates a secure MCP URL that your agents can access to perform actions.

How the Composio SDK works

The Composio SDK follows a three-phase workflow:

  1. Discovery: Searches for tools matching your task and returns relevant toolkits with their details.
  2. Authentication: Checks for active connections. If missing, creates an auth config and returns a connection URL via Auth Link.
  3. Execution: Executes the action using the authenticated connection.

Step-by-step Guide

Step by step09 STEPS
1

Prerequisites

Before starting, make sure you have:
  • Python 3.9 or higher
  • A Composio account with an active API key
  • Basic familiarity with Python and async programming
2

Getting API Keys for OpenAI and Composio

OpenAI API Key
  • Go to the OpenAI dashboard and create an API key. You'll need credits to use the models, or you can connect to another model provider.
  • Keep the API key safe.
Composio API Key
  • Log in to the Composio dashboard.
  • Navigate to your API settings and generate a new API key.
  • Store this key securely as you'll need it for authentication.
3

Install dependencies

bash
pip install composio pydantic-ai python-dotenv

Install the required libraries.

What's happening:

  • composio connects your agent to external SaaS tools like Feathery
  • pydantic-ai lets you create structured AI agents with tool support
  • python-dotenv loads your environment variables securely from a .env file
4

Set up environment variables

bash
COMPOSIO_API_KEY=your_composio_api_key_here
USER_ID=your_user_id_here
OPENAI_API_KEY=your_openai_api_key

Create a .env file in your project root.

What's happening:

  • COMPOSIO_API_KEY authenticates your agent to Composio's API
  • USER_ID associates your session with your account for secure tool access
  • OPENAI_API_KEY to access OpenAI LLMs
5

Import dependencies

python
import asyncio
import os
from dotenv import load_dotenv
from composio import Composio
from pydantic_ai import Agent
from pydantic_ai.mcp import MCPServerStreamableHTTP

load_dotenv()
What's happening:
  • We load environment variables and import required modules
  • Composio manages connections to Feathery
  • MCPServerStreamableHTTP connects to the Feathery MCP server endpoint
  • Agent from Pydantic AI lets you define and run the AI assistant
6

Create a Tool Router Session

python
async def main():
    api_key = os.getenv("COMPOSIO_API_KEY")
    user_id = os.getenv("USER_ID")
    if not api_key or not user_id:
        raise RuntimeError("Set COMPOSIO_API_KEY and USER_ID in your environment")

    # Create a Composio Tool Router session for Feathery
    composio = Composio(api_key=api_key)
    session = composio.create(
        user_id=user_id,
        toolkits=["feathery"],
    )
    url = session.mcp.url
    if not url:
        raise ValueError("Composio session did not return an MCP URL")
What's happening:
  • We're creating a Tool Router session that gives your agent access to Feathery tools
  • The create method takes the user ID and specifies which toolkits should be available
  • The returned session.mcp.url is the MCP server URL that your agent will use
7

Initialize the Pydantic AI Agent

python
# Attach the MCP server to a Pydantic AI Agent
feathery_mcp = MCPServerStreamableHTTP(url, headers={"x-api-key": COMPOSIO_API_KEY})
agent = Agent(
    "openai:gpt-5",
    toolsets=[feathery_mcp],
    instructions=(
        "You are a Feathery assistant. Use Feathery tools to help users "
        "with their requests. Ask clarifying questions when needed."
    ),
)
What's happening:
  • The MCP client connects to the Feathery endpoint
  • The agent uses GPT-5 to interpret user commands and perform Feathery operations
  • The instructions field defines the agent's role and behavior
8

Build the chat interface

python
# Simple REPL with message history
history = []
print("Chat started! Type 'exit' or 'quit' to end.\n")
print("Try asking the agent to help you with Feathery.\n")

while True:
    user_input = input("You: ").strip()
    if user_input.lower() in {"exit", "quit", "bye"}:
        print("\nGoodbye!")
        break
    if not user_input:
        continue

    print("\nAgent is thinking...\n", flush=True)

    async with agent.run_stream(user_input, message_history=history) as stream_result:
        collected_text = ""
        async for chunk in stream_result.stream_output():
            text_piece = None
            if isinstance(chunk, str):
                text_piece = chunk
            elif hasattr(chunk, "delta") and isinstance(chunk.delta, str):
                text_piece = chunk.delta
            elif hasattr(chunk, "text"):
                text_piece = chunk.text
            if text_piece:
                collected_text += text_piece
        result = stream_result

    print(f"Agent: {collected_text}\n")
    history = result.all_messages()
What's happening:
  • The agent reads input from the terminal and streams its response
  • Feathery API calls happen automatically under the hood
  • The model keeps conversation history to maintain context across turns
9

Run the application

python
if __name__ == "__main__":
    asyncio.run(main())
What's happening:
  • The asyncio loop launches the agent and keeps it running until you exit

Complete Code

Here's the complete code to get you started with Feathery and Pydantic AI:

python
import asyncio
import os
from dotenv import load_dotenv
from composio import Composio
from pydantic_ai import Agent
from pydantic_ai.mcp import MCPServerStreamableHTTP

load_dotenv()

async def main():
    api_key = os.getenv("COMPOSIO_API_KEY")
    user_id = os.getenv("USER_ID")
    if not api_key or not user_id:
        raise RuntimeError("Set COMPOSIO_API_KEY and USER_ID in your environment")

    # Create a Composio Tool Router session for Feathery
    composio = Composio(api_key=api_key)
    session = composio.create(
        user_id=user_id,
        toolkits=["feathery"],
    )
    url = session.mcp.url
    if not url:
        raise ValueError("Composio session did not return an MCP URL")

    # Attach the MCP server to a Pydantic AI Agent
    feathery_mcp = MCPServerStreamableHTTP(url, headers={"x-api-key": COMPOSIO_API_KEY})
    agent = Agent(
        "openai:gpt-5",
        toolsets=[feathery_mcp],
        instructions=(
            "You are a Feathery assistant. Use Feathery tools to help users "
            "with their requests. Ask clarifying questions when needed."
        ),
    )

    # Simple REPL with message history
    history = []
    print("Chat started! Type 'exit' or 'quit' to end.\n")
    print("Try asking the agent to help you with Feathery.\n")

    while True:
        user_input = input("You: ").strip()
        if user_input.lower() in {"exit", "quit", "bye"}:
            print("\nGoodbye!")
            break
        if not user_input:
            continue

        print("\nAgent is thinking...\n", flush=True)

        async with agent.run_stream(user_input, message_history=history) as stream_result:
            collected_text = ""
            async for chunk in stream_result.stream_output():
                text_piece = None
                if isinstance(chunk, str):
                    text_piece = chunk
                elif hasattr(chunk, "delta") and isinstance(chunk.delta, str):
                    text_piece = chunk.delta
                elif hasattr(chunk, "text"):
                    text_piece = chunk.text
                if text_piece:
                    collected_text += text_piece
            result = stream_result

        print(f"Agent: {collected_text}\n")
        history = result.all_messages()

if __name__ == "__main__":
    asyncio.run(main())

Conclusion

You've built a Pydantic AI agent that can interact with Feathery through Composio's Tool Router. With this setup, your agent can perform real Feathery actions through natural language. You can extend this further by:
  • Adding other toolkits like Gmail, HubSpot, or Salesforce
  • Building a web-based chat interface around this agent
  • Using multiple MCP endpoints to enable cross-app workflows (for example, Gmail + Feathery for workflow automation)
This architecture makes your AI agent "agent-native", able to securely use APIs in a unified, composable way without custom integrations.
TOOLS

Supported Tools

Every Feathery action and event your agent gets out of the box.

Edit Feathery Account

Tool to edit an existing account’s role and permissions.

Get Account Info

Tool to get your Feathery team name and list of accounts.

Fill or sign document template

Tool to fill or sign a Feathery document template.

List Document Envelopes

Tool to list generated document envelopes by document or user ID.

Create hidden field

Creates a new hidden field in the Feathery account.

Delete Form

Permanently delete a form from your Feathery account.

Get form schema

Retrieve the complete schema of a Feathery form including all steps, fields, rules, and translations.

List Forms

Tool to list all forms in your Feathery account.

List Hidden Fields

Tool to list all hidden form fields in the account.

List API Connector Errors

Tool to list recent API connector error logs for a form.

List Email Issues

Tool to list email bounce and complaint events.

List Email Logs

Tool to list recently sent emails for a form.

List Quik Request Logs

Tool to list recent Quik integration request logs for a form.

Create or Fetch User

Tool to create a new user or fetch an existing one.

Delete User

Tool to delete a specific user by ID.

Get All User Data

Retrieve all stored data fields for a user or all field definitions in your Feathery account.

Get User Session

Tool to get a user's form session and progress.

List Users

Tool to list all users in your Feathery account.

Generate Workspace Login Token

Generate a login JWT for a Feathery workspace.

FRAMEWORKS

How to build Feathery MCP Agent with another framework

OpenAI Agents SDK logo

OpenAI Agents SDK

Use Feathery MCP with OpenAI Agents SDK

Claude Agent SDK logo

Claude Agent SDK

Use Feathery MCP with Claude Agent SDK

Claude Code logo

Claude Code

Use Feathery MCP with Claude Code

Claude Cowork logo

Claude Cowork

Use Feathery MCP with Claude Cowork

Codex logo

Codex

Use Feathery MCP with Codex

OpenClaw logo

OpenClaw

Use Feathery MCP with OpenClaw

Hermes logo

Hermes

Use Feathery MCP with Hermes

CLI logo

CLI

Use Feathery MCP with CLI

Google ADK logo

Google ADK

Use Feathery MCP with Google ADK

LangChain logo

LangChain

Use Feathery MCP with LangChain

Vercel AI SDK logo

Vercel AI SDK

Use Feathery MCP with Vercel AI SDK

Mastra AI logo

Mastra AI

Use Feathery MCP with Mastra AI

LlamaIndex logo

LlamaIndex

Use Feathery MCP with LlamaIndex

CrewAI logo

CrewAI

Use Feathery MCP with CrewAI

FAQ

Frequently asked questions

With a standalone Feathery MCP server, the agents and LLMs can only access a fixed set of Feathery tools tied to that server. However, with the Composio Tool Router, agents can dynamically load tools from Feathery and many other apps based on the task at hand, all through a single MCP endpoint.

Yes, you can. Pydantic AI fully supports MCP integration. You get structured tool calling, message history handling, and model orchestration while Tool Router takes care of discovering and serving the right Feathery tools.

Yes, absolutely. You can configure which Feathery scopes and actions are allowed when connecting your account to Composio. You can also bring your own OAuth credentials or API configuration so you keep full control over what the agent can do.

All sensitive data such as tokens, keys, and configuration is fully encrypted at rest and in transit. Composio is SOC 2 Type 2 compliant and follows strict security practices so your Feathery data and credentials are handled as safely as possible.

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A lot of students tell us that the moment their connected tools start talking to each other inside Opennote feels almost magical. The agent just knows them, and it's immensely helped keep new users on the platform.

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Before Composio, adding tool integrations was slow and resource-intensive. Each one could take weeks of engineering time, and maintaining them meant constantly keeping up with API changes.

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