How to integrate Pdf co MCP with LangChain

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Introduction

This guide walks you through connecting Pdf co to LangChain using the Composio tool router. By the end, you'll have a working Pdf co agent that can extract invoice data from uploaded pdf file, convert excel spreadsheet at url to json, generate a qr code for a payment link, merge multiple pdf files into a single document through natural language commands.

This guide will help you understand how to give your LangChain agent real control over a Pdf co account through Composio's Pdf co 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 Pdf co project to Composio
  • Create a Tool Router MCP session for Pdf co
  • Initialize an MCP client and retrieve Pdf co tools
  • Build a LangChain agent that can interact with Pdf co
  • 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 Pdf co MCP server, and what's possible with it?

The Pdf co MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Pdf co account. It provides structured and secure access to your PDF.co capabilities, so your agent can extract data, generate documents, convert files, process barcodes, and manage asynchronous jobs on your behalf.

  • Automated PDF data extraction and parsing: Let your agent extract structured data from PDFs using templates or parse documents for key information—perfect for receipts, invoices, and more.
  • PDF creation, splitting, and merging: Generate new PDF files, combine multiple PDFs, or split documents into separate files without manual intervention.
  • File format conversion: Seamlessly convert Excel files to CSV, HTML, JSON, text, or XML, enabling efficient data analysis and workflow automation.
  • Barcode generation and processing: Instantly create various barcode formats (QR codes, Code128, PDF417, etc.) or encode data into barcodes for labeling and tracking.
  • Job management and file uploads: Upload documents to PDF.co, track the status of asynchronous jobs, and retrieve results—all through your agent, hands-free.

Supported Tools & Triggers

Tools
Get Account Balance InfoTool to get account balance info.
Generate BarcodeTool to generate barcode images (qr, code128, code39, pdf417, etc.
Convert Excel to CSVTool to convert an excel file (xls/xlsx) to csv.
Convert Excel to HTMLTool to convert an excel file to html.
Convert Excel to JSONTool to convert an online excel or csv file to json format.
Convert Excel to TextTool to convert excel files to plain text.
Convert Excel to XMLTool to convert an excel file to xml.
Document ParserTool to parse documents based on predefined templates to extract structured data.
Upload FileTool to upload a local file to pdf.
Check Job StatusTool to check status and result of an asynchronous job.
Add Content to PDFTool to add content to an existing pdf.
Change PDF Text SearchableTool to make pdf text searchable using ocr.
Delete PDF PagesTool to delete specific pages from a pdf file.
Extract PDF AttachmentsTool to extract embedded attachments from a pdf.
Find Text in PDFTool to find text in a pdf document.
PDF Forms Info ReaderTool to extract form field information from a pdf.
Convert Text to PDFTool to convert plain text data to pdf.
Convert Email to PDFTool to convert email files (.
Convert HTML to PDFTool to convert html code or webpage url into a pdf document.
PDF Info ReaderTool to retrieve detailed information and metadata of a pdf.
Merge PDFsTool to merge multiple pdf files into one document.
Rotate PDF PagesTool to rotate selected pages in a pdf.
Search and Delete Text in PDFTool to search for and delete text in a pdf by keyword or regex.
Search and Replace Text in PDFTool to search for and replace text in a pdf document.
Split PDFTool to split a pdf into multiple files by page ranges.
Convert PDF to CSVTool to convert pdf or scanned images to csv format.
Convert PDF to HTMLTool to convert pdf documents to html.
Convert PDF to ImageTool to convert pdf pages to images (png, jpg, tiff).
Convert PDF to JSONTool to convert pdf or scanned images to json format.
Convert PDF to TextTool to convert pdf or scanned images to plain text.
Convert PDF to XLSTool to convert pdf or scanned images to xls format.
Convert PDF to XLSXTool to convert pdf or scanned images to xlsx (excel) format.
Convert PDF to XMLTool to convert pdf or scanned images to xml format.

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 Pdf co 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 Pdf co tools
  • Validating that COMPOSIO_USER_ID is also set before proceeding

Create a Tool Router session

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

url = session.mcp.url
What's happening:
  • We're creating a Tool Router session that gives your agent access to Pdf co 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 Pdf co tools as needed

Configure the agent with the MCP URL

client = MultiServerMCPClient({
    "pdf_co-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 Pdf co MCP server via HTTP
  • The client is configured with a name and the URL from our Tool Router session
  • get_tools() retrieves all available Pdf co 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 Pdf co 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 Pdf co 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=['pdf_co']
    )

    url = session.mcp.url
    
    client = MultiServerMCPClient({
        "pdf_co-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 Pdf co 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 Pdf co 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 Pdf co MCP Agent with another framework

OpenAI Agents SDK

OpenAI Agents SDK

Use Pdf co MCP with OpenAI Agents SDK

Claude Agent SDK

Claude Agent SDK

Use Pdf co MCP with Claude Agent SDK

Google ADK

Google ADK

Use Pdf co MCP with Google ADK

Vercel AI SDK

Vercel AI SDK

Use Pdf co MCP with Vercel AI SDK

Mastra AI

Mastra AI

Use Pdf co MCP with Mastra AI

LlamaIndex

LlamaIndex

Use Pdf co MCP with LlamaIndex

CrewAI

CrewAI

Use Pdf co MCP with CrewAI

FAQ

What are the differences in Tool Router MCP and Pdf co MCP?

With a standalone Pdf co MCP server, the agents and LLMs can only access a fixed set of Pdf co tools tied to that server. However, with the Composio Tool Router, agents can dynamically load tools from Pdf co 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 Pdf co tools.

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

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

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