How to integrate Docnify MCP with LangChain

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Introduction

This guide walks you through connecting Docnify to LangChain using the Composio tool router. By the end, you'll have a working Docnify agent that can list all documents awaiting my signature, add recipient to contract with specific email, resend nda document to all unsigned recipients, delete expired document from my docnify account through natural language commands.

This guide will help you understand how to give your LangChain agent real control over a Docnify account through Composio's Docnify 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 and set up your OpenAI and Composio API keys
  • Connect your Docnify project to Composio
  • Create a Tool Router MCP session for Docnify
  • Initialize an MCP client and retrieve Docnify tools
  • Build a LangChain agent that can interact with Docnify
  • Set up an interactive chat interface for testing

What is LangChain?

LangChain is a framework for developing applications powered by language models. It provides tools and abstractions for building agents that can reason, use tools, and maintain conversation context.

Key features include:

  • Agent Framework: Build agents that can use tools and make decisions
  • MCP Integration: Connect to external services through Model Context Protocol adapters
  • Memory Management: Maintain conversation history across interactions
  • Multi-Provider Support: Works with OpenAI, Anthropic, and other LLM providers

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

The Docnify MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Docnify account. It provides structured and secure access to your digital signing workflows, so your agent can perform actions like listing documents, adding recipients, creating document fields, and resending signature requests on your behalf.

  • Seamless document management: Let your agent list, fetch, or delete documents in your Docnify account to keep your workspace organized and up to date.
  • Effortless recipient handling: Automatically add or remove recipients from documents, manage their signing order, and control access with a single prompt.
  • Custom field creation and editing: Direct your agent to add or remove fields—like signature, initials, or dates—on documents before sending them out for signing.
  • Template automation: Quickly list available templates or fetch template details, letting your agent streamline the process of generating new documents.
  • Signature request management: Easily resend signature requests or notifications, ensuring signers don’t miss important documents and deadlines.

Supported Tools & Triggers

Tools
Add Document RecipientTool to add a recipient to a document.
Create Document FieldsTool to create one or more fields on a document.
Delete DocumentTool to delete a specific document by its ID.
Delete Document FieldTool to delete a field from a document.
Delete document recipientTool to remove a recipient from a document.
Get DocumentTool to fetch a specific document by its ID.
Get TemplateTool to fetch a specific template by its ID.
List DocumentsTool to list documents in the Docnify account.
List TemplatesTool to list templates in the Docnify account.
Resend DocumentTool to resend a document for signing.
Send DocumentTool to send a document for signing.
Update Document FieldsTool to update metadata of document fields.
Update Document RecipientTool to update a recipient's details (name, email, role) on a document.

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 this tutorial, make sure you have:
  • Python 3.10 or higher installed on your system
  • A Composio account with an API key
  • An OpenAI API key
  • Basic familiarity with Python and async programming

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

pip install composio-langchain langchain-mcp-adapters langchain python-dotenv

Install the required packages for LangChain with MCP support.

What's happening:

  • composio-langchain provides Composio integration for LangChain
  • langchain-mcp-adapters enables MCP client connections
  • langchain is the core agent framework
  • python-dotenv loads environment variables

Set up environment variables

bash
COMPOSIO_API_KEY=your_composio_api_key_here
COMPOSIO_USER_ID=your_composio_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 your requests to Composio's API
  • COMPOSIO_USER_ID identifies the user for session management
  • OPENAI_API_KEY enables access to OpenAI's language models

Import dependencies

from langchain_mcp_adapters.client import MultiServerMCPClient
from langchain.agents import create_agent
from dotenv import load_dotenv
from composio import Composio
import asyncio
import os

load_dotenv()
What's happening:
  • We're importing LangChain's MCP adapter and Composio SDK
  • The dotenv import loads environment variables from your .env file
  • This setup prepares the foundation for connecting LangChain with Docnify functionality through MCP

Initialize Composio client

async def main():
    composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY"))

    if not os.getenv("COMPOSIO_API_KEY"):
        raise ValueError("COMPOSIO_API_KEY is not set")
    if not os.getenv("COMPOSIO_USER_ID"):
        raise ValueError("COMPOSIO_USER_ID is not set")
What's happening:
  • We're loading the COMPOSIO_API_KEY from environment variables and validating it exists
  • Creating a Composio instance that will manage our connection to Docnify tools
  • Validating that COMPOSIO_USER_ID is also set before proceeding

Create a Tool Router session

# Create Tool Router session for Docnify
session = composio.create(
    user_id=os.getenv("COMPOSIO_USER_ID"),
    toolkits=['docnify']
)

url = session.mcp.url
What's happening:
  • We're creating a Tool Router session that gives your agent access to Docnify 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
  • This approach allows the agent to dynamically load and use Docnify tools as needed

Configure the agent with the MCP URL

client = MultiServerMCPClient({
    "docnify-agent": {
        "transport": "streamable_http",
        "url": session.mcp.url,
        "headers": {
            "x-api-key": os.getenv("COMPOSIO_API_KEY")
        }
    }
})

tools = await client.get_tools()

agent = create_agent("gpt-5", tools)
What's happening:
  • We're creating a MultiServerMCPClient that connects to our Docnify MCP server via HTTP
  • The client is configured with a name and the URL from our Tool Router session
  • get_tools() retrieves all available Docnify tools that the agent can use
  • We're creating a LangChain agent using the GPT-5 model

Set up interactive chat interface

conversation_history = []

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

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

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

    if not user_input:
        continue

    conversation_history.append({"role": "user", "content": user_input})
    print("\nAgent is thinking...\n")

    response = await agent.ainvoke({"messages": conversation_history})
    conversation_history = response['messages']
    final_response = response['messages'][-1].content
    print(f"Agent: {final_response}\n")
What's happening:
  • We initialize an empty conversation_history list to maintain context across interactions
  • A while loop continuously accepts user input from the command line
  • When a user types a message, it's added to the conversation history and sent to the agent
  • The agent processes the request using the ainvoke() method with the full conversation history
  • Users can type 'exit', 'quit', or 'bye' to end the chat session gracefully

Run the application

if __name__ == "__main__":
    asyncio.run(main())
What's happening:
  • We call the main() function using asyncio.run() to start the application

Complete Code

Here's the complete code to get you started with Docnify and LangChain:

from langchain_mcp_adapters.client import MultiServerMCPClient
from langchain.agents import create_agent
from dotenv import load_dotenv
from composio import Composio
import asyncio
import os

load_dotenv()

async def main():
    composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY"))
    
    if not os.getenv("COMPOSIO_API_KEY"):
        raise ValueError("COMPOSIO_API_KEY is not set")
    if not os.getenv("COMPOSIO_USER_ID"):
        raise ValueError("COMPOSIO_USER_ID is not set")
    
    session = composio.create(
        user_id=os.getenv("COMPOSIO_USER_ID"),
        toolkits=['docnify']
    )

    url = session.mcp.url
    
    client = MultiServerMCPClient({
        "docnify-agent": {
            "transport": "streamable_http",
            "url": url,
            "headers": {
                "x-api-key": os.getenv("COMPOSIO_API_KEY")
            }
        }
    })
    
    tools = await client.get_tools()
  
    agent = create_agent("gpt-5", tools)
    
    conversation_history = []
    
    print("Chat started! Type 'exit' or 'quit' to end the conversation.\n")
    print("Ask any Docnify related question or task to the agent.\n")
    
    while True:
        user_input = input("You: ").strip()
        
        if user_input.lower() in ['exit', 'quit', 'bye']:
            print("\nGoodbye!")
            break
        
        if not user_input:
            continue
        
        conversation_history.append({"role": "user", "content": user_input})
        print("\nAgent is thinking...\n")
        
        response = await agent.ainvoke({"messages": conversation_history})
        conversation_history = response['messages']
        final_response = response['messages'][-1].content
        print(f"Agent: {final_response}\n")

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

Conclusion

You've successfully built a LangChain agent that can interact with Docnify through Composio's Tool Router.

Key features of this implementation:

  • Dynamic tool loading through Composio's Tool Router
  • Conversation history maintenance for context-aware responses
  • Async Python provides clean, efficient execution of agent workflows
You can extend this further by adding error handling, implementing specific business logic, or integrating additional Composio toolkits to create multi-app workflows.

How to build Docnify MCP Agent with another framework

OpenAI Agents SDK

OpenAI Agents SDK

Use Docnify MCP with OpenAI Agents SDK

Claude Agent SDK

Claude Agent SDK

Use Docnify MCP with Claude Agent SDK

Google ADK

Google ADK

Use Docnify MCP with Google ADK

Vercel AI SDK

Vercel AI SDK

Use Docnify MCP with Vercel AI SDK

Mastra AI

Mastra AI

Use Docnify MCP with Mastra AI

LlamaIndex

LlamaIndex

Use Docnify MCP with LlamaIndex

CrewAI

CrewAI

Use Docnify MCP with CrewAI

FAQ

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

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

Can I use Tool Router MCP with LangChain?

Yes, you can. LangChain 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 Docnify tools.

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

Yes, absolutely. You can configure which Docnify 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 Docnify data and credentials are handled as safely as possible.

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