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How to integrate Feathery MCP with CrewAI

Connect CrewAI to Feathery MCP. List all forms created this month, get schema for user registration form, and more using natural language, with authentication handled for you.

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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.

19 Tools

Introduction

This guide walks you through connecting Feathery to CrewAI using the Composio tool router. By the end, you'll have a working Feathery agent that can browse forms and schemas, fill or sign document templates, create hidden fields, manage users, and review email logs through natural language commands.

This guide will help you understand how to give your CrewAI 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

Claude CodeClaude CoworkOpenAI Agents SDKClaude Agent SDKCodexOpenClawHermesAtomic AgentGoogle ADKLangChainVercel AI SDKMastra AILlamaIndexPydantic AIAutoGen

TL;DR

Here's what you'll learn:
  • Get a Composio API key and configure your Feathery connection
  • Set up CrewAI with an MCP enabled agent
  • Create a Tool Router session or standalone MCP server for Feathery
  • Build a conversational loop where your agent can execute Feathery operations

What is CrewAI?

CrewAI is a powerful framework for building multi-agent AI systems. It provides primitives for defining agents with specific roles, creating tasks, and orchestrating workflows through crews.

Key features include:

  • Agent Roles: Define specialized agents with specific goals and backstories
  • Task Management: Create tasks with clear descriptions and expected outputs
  • Crew Orchestration: Combine agents and tasks into collaborative workflows
  • MCP Integration: Connect to external tools through Model Context Protocol

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

Before starting, make sure you have:
  • Python 3.9 or higher
  • A Composio account and API key
  • A Feathery connection authorized in Composio
  • An OpenAI API key for the CrewAI LLM
  • Basic familiarity with Python
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 crewai crewai-tools[mcp] python-dotenv
What's happening:
  • composio connects your agent to Feathery via MCP
  • crewai provides Agent, Task, Crew, and LLM primitives
  • crewai-tools[mcp] includes MCP helpers
  • python-dotenv loads environment variables from .env
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_here

Create a .env file in your project root.

What's happening:

  • COMPOSIO_API_KEY authenticates with Composio
  • USER_ID scopes the session to your account
  • OPENAI_API_KEY lets CrewAI use your chosen OpenAI model
5

Import dependencies

python
import os
from composio import Composio
from crewai import Agent, Task, Crew
from crewai_tools import MCPServerAdapter
import dotenv

dotenv.load_dotenv()

COMPOSIO_API_KEY = os.getenv("COMPOSIO_API_KEY")
COMPOSIO_USER_ID = os.getenv("COMPOSIO_USER_ID")

if not COMPOSIO_API_KEY:
    raise ValueError("COMPOSIO_API_KEY is not set")
if not COMPOSIO_USER_ID:
    raise ValueError("COMPOSIO_USER_ID is not set")
What's happening:
  • CrewAI classes define agents and tasks, and run the workflow
  • MCPServerHTTP connects the agent to an MCP endpoint
  • Composio will give you a short lived Feathery MCP URL
6

Create a Composio Tool Router session for Feathery

python
composio_client = Composio(api_key=COMPOSIO_API_KEY)
session = composio_client.create(user_id=COMPOSIO_USER_ID, toolkits=["feathery"])

url = session.mcp.url
What's happening:
  • You create a Feathery only session through Composio
  • Composio returns an MCP HTTP URL that exposes Feathery tools
7

Initialize the MCP Server

python
server_params = {
    "url": url,
    "transport": "streamable-http",
    "headers": {"x-api-key": COMPOSIO_API_KEY},
}

with MCPServerAdapter(server_params) as tools:
    agent = Agent(
        role="Search Assistant",
        goal="Help users search the internet effectively",
        backstory="You are a helpful assistant with access to search tools.",
        tools=tools,
        verbose=False,
        max_iter=10,
    )
What's Happening:
  • Server Configuration: The code sets up connection parameters including the MCP server URL, streamable HTTP transport, and Composio API key authentication.
  • MCP Adapter Bridge: MCPServerAdapter acts as a context manager that converts Composio MCP tools into a CrewAI-compatible format.
  • Agent Setup: Creates a CrewAI Agent with a defined role (Search Assistant), goal (help with internet searches), and access to the MCP tools.
  • Configuration Options: The agent includes settings like verbose=False for clean output and max_iter=10 to prevent infinite loops.
  • Dynamic Tool Usage: Once created, the agent automatically accesses all Composio Search tools and decides when to use them based on user queries.
8

Create a CLI Chatloop and define the Crew

python
print("Chat started! Type 'exit' or 'quit' to end.\n")

conversation_context = ""

while True:
    user_input = input("You: ").strip()

    if user_input.lower() in ["exit", "quit", "bye"]:
        print("\nGoodbye!")
        break

    if not user_input:
        continue

    conversation_context += f"\nUser: {user_input}\n"
    print("\nAgent is thinking...\n")

    task = Task(
        description=(
            f"Conversation history:\n{conversation_context}\n\n"
            f"Current request: {user_input}"
        ),
        expected_output="A helpful response addressing the user's request",
        agent=agent,
    )

    crew = Crew(agents=[agent], tasks=[task], verbose=False)
    result = crew.kickoff()
    response = str(result)

    conversation_context += f"Agent: {response}\n"
    print(f"Agent: {response}\n")
What's Happening:
  • Interactive CLI Setup: The code creates an infinite loop that continuously prompts for user input and maintains the entire conversation history in a string variable.
  • Input Validation: Empty inputs are ignored to prevent processing blank messages and keep the conversation clean.
  • Context Building: Each user message is appended to the conversation context, which preserves the full dialogue history for better agent responses.
  • Dynamic Task Creation: For every user input, a new Task is created that includes both the full conversation history and the current request as context.
  • Crew Execution: A Crew is instantiated with the agent and task, then kicked off to process the request and generate a response.
  • Response Management: The agent's response is converted to a string, added to the conversation context, and displayed to the user, maintaining conversational continuity.

Complete Code

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

python
from crewai import Agent, Task, Crew, LLM
from crewai_tools import MCPServerAdapter
from composio import Composio
from dotenv import load_dotenv
import os

load_dotenv()

GOOGLE_API_KEY = os.getenv("GOOGLE_API_KEY")
COMPOSIO_API_KEY = os.getenv("COMPOSIO_API_KEY")
COMPOSIO_USER_ID = os.getenv("COMPOSIO_USER_ID")

if not GOOGLE_API_KEY:
    raise ValueError("GOOGLE_API_KEY is not set in the environment.")
if not COMPOSIO_API_KEY:
    raise ValueError("COMPOSIO_API_KEY is not set in the environment.")
if not COMPOSIO_USER_ID:
    raise ValueError("COMPOSIO_USER_ID is not set in the environment.")

# Initialize Composio and create a session
composio = Composio(api_key=COMPOSIO_API_KEY)
session = composio.create(
    user_id=COMPOSIO_USER_ID,
    toolkits=["feathery"],
)
url = session.mcp.url

# Configure LLM
llm = LLM(
    model="gpt-5",
    api_key=os.getenv("OPENAI_API_KEY"),
)

server_params = {
    "url": url,
    "transport": "streamable-http",
    "headers": {"x-api-key": COMPOSIO_API_KEY},
}

with MCPServerAdapter(server_params) as tools:
    agent = Agent(
        role="Search Assistant",
        goal="Help users with internet searches",
        backstory="You are an expert assistant with access to Composio Search tools.",
        tools=tools,
        llm=llm,
        verbose=False,
        max_iter=10,
    )

    print("Chat started! Type 'exit' or 'quit' to end.\n")

    conversation_context = ""

    while True:
        user_input = input("You: ").strip()

        if user_input.lower() in ["exit", "quit", "bye"]:
            print("\nGoodbye!")
            break

        if not user_input:
            continue

        conversation_context += f"\nUser: {user_input}\n"
        print("\nAgent is thinking...\n")

        task = Task(
            description=(
                f"Conversation history:\n{conversation_context}\n\n"
                f"Current request: {user_input}"
            ),
            expected_output="A helpful response addressing the user's request",
            agent=agent,
        )

        crew = Crew(agents=[agent], tasks=[task], verbose=False)
        result = crew.kickoff()
        response = str(result)

        conversation_context += f"Agent: {response}\n"
        print(f"Agent: {response}\n")

Conclusion

You now have a CrewAI agent connected to Feathery through Composio's Tool Router. The agent can perform Feathery operations through natural language commands.

Next steps:

  • Add role-specific instructions to customize agent behavior
  • Plug in more toolkits for multi-app workflows
  • Chain tasks for complex multi-step operations
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

Fill a mapped Feathery document template and optionally route it to an email address for signature.

List Document Envelopes

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

Create hidden field

Create a hidden field in a Feathery account to store non-visible data such as tracking or integration metadata.

Delete Form

Permanently delete a form from your Feathery account.

Get form schema

Retrieve the schema of a Feathery form by its form ID, including its steps, fields, rules, integrations, 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

List recent API connector error logs for a form, optionally filtered by time.

List Email Issues

Tool to list email bounce and complaint events.

List Email Logs

List recently sent email integration logs for a form, optionally filtered by time.

List Quik Request Logs

List recent Quik integration request logs for a form, optionally filtered by time.

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

Claude Code logo

Claude Code

Use Feathery MCP with Claude Code

Claude Cowork logo

Claude Cowork

Use Feathery MCP with Claude Cowork

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

Codex logo

Codex

Use Feathery MCP with Codex

OpenClaw logo

OpenClaw

Use Feathery MCP with OpenClaw

Hermes logo

Hermes

Use Feathery MCP with Hermes

Atomic Agent logo

Atomic Agent

Use Feathery MCP with Atomic Agent

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

Pydantic AI logo

Pydantic AI

Use Feathery MCP with Pydantic AI

AutoGen logo

AutoGen

Use Feathery MCP with AutoGen

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. CrewAI 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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