How to integrate Signwell MCP with CrewAI

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Introduction

This guide walks you through connecting Signwell to CrewAI using the Composio tool router. By the end, you'll have a working Signwell agent that can send nda document for signature to client, list all pending documents awaiting signatures, delete a document that was sent by mistake, register webhook to monitor completed signings through natural language commands.

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

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

TL;DR

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

The Signwell MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Signwell account. It provides structured and secure access to your document signing workflow, so your agent can perform actions like sending signature requests, managing documents, handling webhooks, and retrieving account info on your behalf.

  • Automated document creation and sending: Have your agent generate new documents and instantly send them out for e-signature—no manual uploads or email juggling required.
  • Real-time document status retrieval: Let your agent fetch the latest status and details of any document, so you always know who has signed and what’s still pending.
  • Bulk send management: Effortlessly list and track large batches of signature requests, helping you stay organized when sending documents to multiple recipients.
  • Webhook automation and monitoring: Register or remove webhooks so your agent can monitor document events and respond instantly to completed, signed, or cancelled documents.
  • Credential and account validation: Quickly check and verify your Signwell account details and API credentials directly through your agent.

Supported Tools & Triggers

Tools
Create DocumentTool to create and optionally send a new document for signing.
Create WebhookTool to register a webhook callback url.
Delete DocumentTool to delete a document (and cancel signing if in progress).
Delete WebhookTool to delete a registered webhook.
Get CredentialsTool to retrieve account information for the current api key.
Get DocumentTool to return a document and all associated document data.
List Bulk SendsTool to list all bulk sends.
List WebhooksTool to list all registered webhooks.

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

What is Tool Router?

Composio's Tool Router 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 Tool Router

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

How the Tool Router works

The Tool Router 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

Prerequisites

Before starting, make sure you have:
  • Python 3.9 or higher
  • A Composio account and API key
  • A Signwell connection authorized in Composio
  • An OpenAI API key for the CrewAI LLM
  • Basic familiarity with Python

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.

Install dependencies

bash
pip install composio crewai crewai-tools python-dotenv
What's happening:
  • composio connects your agent to Signwell via MCP
  • crewai provides Agent, Task, Crew, and LLM primitives
  • crewai-tools includes MCP helpers
  • python-dotenv loads environment variables from .env

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

Import dependencies

python
from crewai import Agent, Task, Crew, LLM
from crewai_tools import MCPServerAdapter  # optional import if you plan to adapt tools
from composio import Composio
from dotenv import load_dotenv
import os
from crewai.mcp import MCPServerHTTP

load_dotenv()
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 Signwell MCP URL

Create a Composio Tool Router session for Signwell

python
composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY"))
session = composio.create(
    user_id=os.getenv("USER_ID"),
    toolkits=["signwell"],
)
url = session.mcp.url
What's happening:
  • You create a Signwell only session through Composio
  • Composio returns an MCP HTTP URL that exposes Signwell tools

Configure the LLM

python
llm = LLM(
    model="gpt-5-mini",
    api_key=os.getenv("OPENAI_API_KEY"),
)
What's happening:
  • CrewAI will call this LLM for planning and responses
  • You can swap in a different model if needed

Attach the MCP server and create the agent

python
toolkit_agent = Agent(
    role="Signwell Assistant",
    goal="Help users interact with Signwell through natural language commands",
    backstory=(
        "You are an expert assistant with access to Signwell tools. "
        "You can perform various Signwell operations on behalf of the user."
    ),
    mcps=[
        MCPServerHTTP(
            url=url,
            streamable=True,
            cache_tools_list=True,
            headers={"x-api-key": os.getenv("COMPOSIO_API_KEY")},
        ),
    ],
    llm=llm,
    verbose=True,
    max_iter=10,
)
What's happening:
  • MCPServerHTTP connects the agent to the Signwell MCP endpoint
  • cache_tools_list saves a tools catalog for faster subsequent runs
  • verbose helps you see what the agent is doing

Add a REPL loop with Task and Crew

python
print("Chat started! Type 'exit' or 'quit' to end.\n")
print("Try asking the agent to perform Signwell operations.\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"Based on the conversation history:\n{conversation_context}\n\n"
            f"Current user request: {user_input}\n\n"
            f"Please help the user with their Signwell related request."
        ),
        expected_output="A helpful response addressing the user's request",
        agent=toolkit_agent,
    )

    crew = Crew(
        agents=[toolkit_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:
  • You build a simple chat loop and keep a running context
  • Each user turn becomes a Task handled by the same agent
  • Crew executes the task and returns a response

Run the application

python
if __name__ == "__main__":
    main()
What's happening:
  • Standard Python entry point so you can run python crewai_signwell_agent.py

Complete Code

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

python
# file: crewai_signwell_agent.py
from crewai import Agent, Task, Crew, LLM
from crewai_tools import MCPServerAdapter  # optional
from composio import Composio
from dotenv import load_dotenv
import os
from crewai.mcp import MCPServerHTTP

load_dotenv()

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

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

    # Create Signwell assistant agent
    toolkit_agent = Agent(
        role="Signwell Assistant",
        goal="Help users interact with Signwell through natural language commands",
        backstory=(
            "You are an expert assistant with access to Signwell tools. "
            "You can perform various Signwell operations on behalf of the user."
        ),
        mcps=[
            MCPServerHTTP(
                url=url,
                streamable=True,
                cache_tools_list=True,
                headers={"x-api-key": os.getenv("COMPOSIO_API_KEY")},
            ),
        ],
        llm=llm,
        verbose=True,
        max_iter=10,
    )

    print("Chat started! Type 'exit' or 'quit' to end the conversation.\n")
    print("Try asking the agent to perform Signwell operations.\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"Based on the conversation history:\n{conversation_context}\n\n"
                f"Current user request: {user_input}\n\n"
                f"Please help the user with their Signwell related request."
            ),
            expected_output="A helpful response addressing the user's request",
            agent=toolkit_agent,
        )

        crew = Crew(
            agents=[toolkit_agent],
            tasks=[task],
            verbose=False,
        )

        result = crew.kickoff()
        response = str(result)

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

if __name__ == "__main__":
    main()

Conclusion

You now have a CrewAI agent connected to Signwell through Composio's Tool Router. The agent can perform Signwell 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

How to build Signwell MCP Agent with another framework

OpenAI Agents SDK

OpenAI Agents SDK

Use Signwell MCP with OpenAI Agents SDK

Claude Agent SDK

Claude Agent SDK

Use Signwell MCP with Claude Agent SDK

Google ADK

Google ADK

Use Signwell MCP with Google ADK

LangChain

LangChain

Use Signwell MCP with LangChain

Vercel AI SDK

Vercel AI SDK

Use Signwell MCP with Vercel AI SDK

Mastra AI

Mastra AI

Use Signwell MCP with Mastra AI

LlamaIndex

LlamaIndex

Use Signwell MCP with LlamaIndex

FAQ

What are the differences in Tool Router MCP and Signwell MCP?

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

Can I use Tool Router MCP with CrewAI?

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 Signwell tools.

Can I manage the permissions and scopes for Signwell while using Tool Router?

Yes, absolutely. You can configure which Signwell 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.

How safe is my data with Composio Tool Router?

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 Signwell data and credentials are handled as safely as possible.

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