How to integrate Feathery MCP with Autogen

This guide walks you through connecting Feathery to AutoGen 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 AutoGen 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 AutoGen 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 AutoGen 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:
  • Get and set up your OpenAI and Composio API keys
  • Install the required dependencies for Autogen and Composio
  • Initialize Composio and create a Tool Router session for Feathery
  • Wire that MCP URL into Autogen using McpWorkbench and StreamableHttpServerParams
  • Configure an Autogen AssistantAgent that can call Feathery tools
  • Run a live chat loop where you ask the agent to perform Feathery operations

What is AutoGen?

Autogen is a framework for building multi-agent conversational AI systems from Microsoft. It enables you to create agents that can collaborate, use tools, and maintain complex workflows.

Key features include:

  • Multi-Agent Systems: Build collaborative agent workflows
  • MCP Workbench: Native support for Model Context Protocol tools
  • Streaming HTTP: Connect to external services through streamable HTTP
  • AssistantAgent: Pre-built agent class for tool-using assistants

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 step08 STEPS
1

Prerequisites

You will need:

  • A Composio API key
  • An OpenAI API key (used by Autogen's OpenAIChatCompletionClient)
  • A Feathery account you can connect to Composio
  • Some basic familiarity with Autogen and Python async
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 python-dotenv
pip install autogen-agentchat autogen-ext-openai autogen-ext-tools

Install Composio, Autogen extensions, and dotenv.

What's happening:

  • composio connects your agent to Feathery via MCP
  • autogen-agentchat provides the AssistantAgent class
  • autogen-ext-openai provides the OpenAI model client
  • autogen-ext-tools provides MCP workbench support

4

Set up environment variables

bash
COMPOSIO_API_KEY=your-composio-api-key
OPENAI_API_KEY=your-openai-api-key
USER_ID=your-user-identifier@example.com

Create a .env file in your project folder.

What's happening:

  • COMPOSIO_API_KEY is required to talk to Composio
  • OPENAI_API_KEY is used by Autogen's OpenAI client
  • USER_ID is how Composio identifies which user's Feathery connections to use
5

Import dependencies and create Tool Router session

python
import asyncio
import os
from dotenv import load_dotenv
from composio import Composio

from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient
from autogen_ext.tools.mcp import McpWorkbench, StreamableHttpServerParams

load_dotenv()

async def main():
    # Initialize Composio and create a Feathery session
    composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY"))
    session = composio.create(
        user_id=os.getenv("USER_ID"),
        toolkits=["feathery"]
    )
    url = session.mcp.url
What's happening:
  • load_dotenv() reads your .env file
  • Composio(api_key=...) initializes the SDK
  • create(...) creates a Tool Router session that exposes Feathery tools
  • session.mcp.url is the MCP endpoint that Autogen will connect to
6

Configure MCP parameters for Autogen

python
# Configure MCP server parameters for Streamable HTTP
server_params = StreamableHttpServerParams(
    url=url,
    timeout=30.0,
    sse_read_timeout=300.0,
    terminate_on_close=True,
    headers={"x-api-key": os.getenv("COMPOSIO_API_KEY")}
)

Autogen expects parameters describing how to talk to the MCP server. That is what StreamableHttpServerParams is for.

What's happening:

  • url points to the Tool Router MCP endpoint from Composio
  • timeout is the HTTP timeout for requests
  • sse_read_timeout controls how long to wait when streaming responses
  • terminate_on_close=True cleans up the MCP server process when the workbench is closed
7

Create the model client and agent

python
# Create model client
model_client = OpenAIChatCompletionClient(
    model="gpt-5",
    api_key=os.getenv("OPENAI_API_KEY")
)

# Use McpWorkbench as context manager
async with McpWorkbench(server_params) as workbench:
    # Create Feathery assistant agent with MCP tools
    agent = AssistantAgent(
        name="feathery_assistant",
        description="An AI assistant that helps with Feathery operations.",
        model_client=model_client,
        workbench=workbench,
        model_client_stream=True,
        max_tool_iterations=10
    )

What's happening:

  • OpenAIChatCompletionClient wraps the OpenAI model for Autogen
  • McpWorkbench connects the agent to the MCP tools
  • AssistantAgent is configured with the Feathery tools from the workbench
8

Run the interactive chat loop

python
print("Chat started! Type 'exit' or 'quit' to end the conversation.\n")
print("Ask any Feathery related question or task to the agent.\n")

# Conversation loop
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")

    # Run the agent with streaming
    try:
        response_text = ""
        async for message in agent.run_stream(task=user_input):
            if hasattr(message, "content") and message.content:
                response_text = message.content

        # Print the final response
        if response_text:
            print(f"Agent: {response_text}\n")
        else:
            print("Agent: I encountered an issue processing your request.\n")

    except Exception as e:
        print(f"Agent: Sorry, I encountered an error: {str(e)}\n")
What's happening:
  • The script prompts you in a loop with You:
  • Autogen passes your input to the model, which decides which Feathery tools to call via MCP
  • agent.run_stream(...) yields streaming messages as the agent thinks and calls tools
  • Typing exit, quit, or bye ends the loop

Complete Code

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

python
import asyncio
import os
from dotenv import load_dotenv
from composio import Composio

from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient
from autogen_ext.tools.mcp import McpWorkbench, StreamableHttpServerParams

load_dotenv()

async def main():
    # Initialize Composio and create a Feathery session
    composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY"))
    session = composio.create(
        user_id=os.getenv("USER_ID"),
        toolkits=["feathery"]
    )
    url = session.mcp.url

    # Configure MCP server parameters for Streamable HTTP
    server_params = StreamableHttpServerParams(
        url=url,
        timeout=30.0,
        sse_read_timeout=300.0,
        terminate_on_close=True,
        headers={"x-api-key": os.getenv("COMPOSIO_API_KEY")}
    )

    # Create model client
    model_client = OpenAIChatCompletionClient(
        model="gpt-5",
        api_key=os.getenv("OPENAI_API_KEY")
    )

    # Use McpWorkbench as context manager
    async with McpWorkbench(server_params) as workbench:
        # Create Feathery assistant agent with MCP tools
        agent = AssistantAgent(
            name="feathery_assistant",
            description="An AI assistant that helps with Feathery operations.",
            model_client=model_client,
            workbench=workbench,
            model_client_stream=True,
            max_tool_iterations=10
        )

        print("Chat started! Type 'exit' or 'quit' to end the conversation.\n")
        print("Ask any Feathery related question or task to the agent.\n")

        # Conversation loop
        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")

            # Run the agent with streaming
            try:
                response_text = ""
                async for message in agent.run_stream(task=user_input):
                    if hasattr(message, 'content') and message.content:
                        response_text = message.content

                # Print the final response
                if response_text:
                    print(f"Agent: {response_text}\n")
                else:
                    print("Agent: I encountered an issue processing your request.\n")

            except Exception as e:
                print(f"Agent: Sorry, I encountered an error: {str(e)}\n")

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

Conclusion

You now have an Autogen assistant wired into Feathery through Composio's Tool Router and MCP. From here you can:
  • Add more toolkits to the toolkits list, for example notion or hubspot
  • Refine the agent description to point it at specific workflows
  • Wrap this script behind a UI, Slack bot, or internal tool
Once the pattern is clear for Feathery, you can reuse the same structure for other MCP-enabled apps with minimal code changes.
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. Autogen 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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