How to integrate Pdf co MCP with Pydantic AI

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Introduction

This guide walks you through connecting Pdf co to Pydantic AI 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 Pydantic AI 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:
  • How to set up your Composio API key and User ID
  • How to create a Composio Tool Router session for Pdf co
  • How to attach an MCP Server to a Pydantic AI agent
  • How to stream responses and maintain chat history
  • How to build a simple REPL-style chat interface to test your Pdf co workflows

What is Pydantic AI?

Pydantic AI is a Python framework for building AI agents with strong typing and validation. It leverages Pydantic's data validation capabilities to create robust, type-safe AI applications.

Key features include:

  • Type Safety: Built on Pydantic for automatic data validation
  • MCP Support: Native support for Model Context Protocol servers
  • Streaming: Built-in support for streaming responses
  • Async First: Designed for async/await patterns

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, make sure you have:
  • Python 3.9 or higher
  • A Composio account with an active 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

bash
pip install composio pydantic-ai python-dotenv

Install the required libraries.

What's happening:

  • composio connects your agent to external SaaS tools like Pdf co
  • pydantic-ai lets you create structured AI agents with tool support
  • python-dotenv loads your environment variables securely from a .env file

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

Create a .env file in your project root.

What's happening:

  • COMPOSIO_API_KEY authenticates your agent to Composio's API
  • USER_ID associates your session with your account for secure tool access
  • OPENAI_API_KEY to access OpenAI LLMs

Import dependencies

python
import asyncio
import os
from dotenv import load_dotenv
from composio import Composio
from pydantic_ai import Agent
from pydantic_ai.mcp import MCPServerStreamableHTTP

load_dotenv()
What's happening:
  • We load environment variables and import required modules
  • Composio manages connections to Pdf co
  • MCPServerStreamableHTTP connects to the Pdf co MCP server endpoint
  • Agent from Pydantic AI lets you define and run the AI assistant

Create a Tool Router Session

python
async def main():
    api_key = os.getenv("COMPOSIO_API_KEY")
    user_id = os.getenv("USER_ID")
    if not api_key or not user_id:
        raise RuntimeError("Set COMPOSIO_API_KEY and USER_ID in your environment")

    # Create a Composio Tool Router session for Pdf co
    composio = Composio(api_key=api_key)
    session = composio.create(
        user_id=user_id,
        toolkits=["pdf_co"],
    )
    url = session.mcp.url
    if not url:
        raise ValueError("Composio session did not return an 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

Initialize the Pydantic AI Agent

python
# Attach the MCP server to a Pydantic AI Agent
pdf_co_mcp = MCPServerStreamableHTTP(url, headers={"x-api-key": COMPOSIO_API_KEY})
agent = Agent(
    "openai:gpt-5",
    toolsets=[pdf_co_mcp],
    instructions=(
        "You are a Pdf co assistant. Use Pdf co tools to help users "
        "with their requests. Ask clarifying questions when needed."
    ),
)
What's happening:
  • The MCP client connects to the Pdf co endpoint
  • The agent uses GPT-5 to interpret user commands and perform Pdf co operations
  • The instructions field defines the agent's role and behavior

Build the chat interface

python
# Simple REPL with message history
history = []
print("Chat started! Type 'exit' or 'quit' to end.\n")
print("Try asking the agent to help you with Pdf co.\n")

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", flush=True)

    async with agent.run_stream(user_input, message_history=history) as stream_result:
        collected_text = ""
        async for chunk in stream_result.stream_output():
            text_piece = None
            if isinstance(chunk, str):
                text_piece = chunk
            elif hasattr(chunk, "delta") and isinstance(chunk.delta, str):
                text_piece = chunk.delta
            elif hasattr(chunk, "text"):
                text_piece = chunk.text
            if text_piece:
                collected_text += text_piece
        result = stream_result

    print(f"Agent: {collected_text}\n")
    history = result.all_messages()
What's happening:
  • The agent reads input from the terminal and streams its response
  • Pdf co API calls happen automatically under the hood
  • The model keeps conversation history to maintain context across turns

Run the application

python
if __name__ == "__main__":
    asyncio.run(main())
What's happening:
  • The asyncio loop launches the agent and keeps it running until you exit

Complete Code

Here's the complete code to get you started with Pdf co and Pydantic AI:

import asyncio
import os
from dotenv import load_dotenv
from composio import Composio
from pydantic_ai import Agent
from pydantic_ai.mcp import MCPServerStreamableHTTP

load_dotenv()

async def main():
    api_key = os.getenv("COMPOSIO_API_KEY")
    user_id = os.getenv("USER_ID")
    if not api_key or not user_id:
        raise RuntimeError("Set COMPOSIO_API_KEY and USER_ID in your environment")

    # Create a Composio Tool Router session for Pdf co
    composio = Composio(api_key=api_key)
    session = composio.create(
        user_id=user_id,
        toolkits=["pdf_co"],
    )
    url = session.mcp.url
    if not url:
        raise ValueError("Composio session did not return an MCP URL")

    # Attach the MCP server to a Pydantic AI Agent
    pdf_co_mcp = MCPServerStreamableHTTP(url, headers={"x-api-key": COMPOSIO_API_KEY})
    agent = Agent(
        "openai:gpt-5",
        toolsets=[pdf_co_mcp],
        instructions=(
            "You are a Pdf co assistant. Use Pdf co tools to help users "
            "with their requests. Ask clarifying questions when needed."
        ),
    )

    # Simple REPL with message history
    history = []
    print("Chat started! Type 'exit' or 'quit' to end.\n")
    print("Try asking the agent to help you with Pdf co.\n")

    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", flush=True)

        async with agent.run_stream(user_input, message_history=history) as stream_result:
            collected_text = ""
            async for chunk in stream_result.stream_output():
                text_piece = None
                if isinstance(chunk, str):
                    text_piece = chunk
                elif hasattr(chunk, "delta") and isinstance(chunk.delta, str):
                    text_piece = chunk.delta
                elif hasattr(chunk, "text"):
                    text_piece = chunk.text
                if text_piece:
                    collected_text += text_piece
            result = stream_result

        print(f"Agent: {collected_text}\n")
        history = result.all_messages()

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

Conclusion

You've built a Pydantic AI agent that can interact with Pdf co through Composio's Tool Router. With this setup, your agent can perform real Pdf co actions through natural language. You can extend this further by:
  • Adding other toolkits like Gmail, HubSpot, or Salesforce
  • Building a web-based chat interface around this agent
  • Using multiple MCP endpoints to enable cross-app workflows (for example, Gmail + Pdf co for workflow automation)
This architecture makes your AI agent "agent-native", able to securely use APIs in a unified, composable way without custom integrations.

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

LangChain

LangChain

Use Pdf co MCP with LangChain

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 Pydantic AI?

Yes, you can. Pydantic AI 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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