How to integrate Pdfmonkey MCP with OpenAI Agents SDK

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Introduction

This guide walks you through connecting Pdfmonkey to the OpenAI Agents SDK using the Composio tool router. By the end, you'll have a working Pdfmonkey agent that can generate pdf invoices from my template, download latest generated contract pdf file, create a new proposal template for sales, delete old pdf documents by document id through natural language commands.

This guide will help you understand how to give your OpenAI Agents SDK agent real control over a Pdfmonkey account through Composio's Pdfmonkey 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
  • Install the necessary dependencies
  • Initialize Composio and create a Tool Router session for Pdfmonkey
  • Configure an AI agent that can use Pdfmonkey as a tool
  • Run a live chat session where you can ask the agent to perform Pdfmonkey operations

What is open-ai-agents-sdk?

The OpenAI Agents SDK is a lightweight framework for building AI agents that can use tools and maintain conversation state. It provides a simple interface for creating agents with hosted MCP tool support.

Key features include:

  • Hosted MCP Tools: Connect to external services through hosted MCP endpoints
  • SQLite Sessions: Persist conversation history across interactions
  • Simple API: Clean interface with Agent, Runner, and tool configuration
  • Streaming Support: Real-time response streaming for interactive applications

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

The Pdfmonkey MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, or others directly to your Pdfmonkey account. It provides structured and secure access to your PDF automation workflows, so your agent can generate documents from templates, download PDFs, manage templates, and retrieve document details on your behalf.

  • Automated PDF generation: Instantly create new PDF documents from pre-built templates or custom data payloads, either asynchronously or waiting for immediate results.
  • Template management and updates: Let your agent create, fetch, or delete document templates to keep your PDF generation process organized and up to date.
  • Document retrieval and monitoring: Fetch the full details of any generated document, including metadata, logs, and download links for seamless workflow integration.
  • Secure PDF file download: Easily obtain presigned URLs to access or share generated PDF files, with automatic handling of expiring links.
  • Account and usage insights: Retrieve authenticated user information, such as quota, plan, and locale, to help monitor and manage your Pdfmonkey usage directly from your agent.

Supported Tools & Triggers

Tools
Create DocumentTool to create a Document.
Create Document SyncTool to create a document and wait for generation to finish.
Create TemplateTool to create a new Document Template.
Delete DocumentTool to delete a Document by its ID.
Delete PDFMonkey Document TemplateTool to delete a document template by ID.
Download Document FileTool to download a generated PDF file via a presigned URL.
Get Current UserTool to retrieve details about the currently authenticated user.
Get DocumentTool to fetch a Document by its ID.
Get DocumentCardTool to fetch a DocumentCard by ID.
Get Template by IDTool to fetch a Document Template by ID.
List DocumentCardsTool to list DocumentCards.
List PDF EnginesTool to list available PDF engines with deprecation metadata.
List Template CardsTool to list template cards for a workspace.
List WorkspacesTool to list workspaces (applications).
Preview DocumentTool to open a document preview via a PDF.
Preview TemplateTool to preview a template draft as a real PDF via the preview_url.
Update DocumentTool to update a Document’s payload, meta, or status.
Update Document TemplateTool to update a document template’s properties.
View Public Share LinkTool to download a publicly shared PDF via its permanent share link.

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:
  • Composio API Key and OpenAI API Key
  • Primary know-how of OpenAI Agents SDK
  • A live Pdfmonkey project
  • Some knowledge of Python or Typescript

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

Install dependencies

pip install composio_openai_agents openai-agents python-dotenv

Install the Composio SDK and the OpenAI Agents SDK.

Set up environment variables

bash
OPENAI_API_KEY=sk-...your-api-key
COMPOSIO_API_KEY=your-api-key
USER_ID=composio_user@gmail.com

Create a .env file and add your OpenAI and Composio API keys.

Import dependencies

import asyncio
import os
from dotenv import load_dotenv

from composio import Composio
from composio_openai_agents import OpenAIAgentsProvider
from agents import Agent, Runner, HostedMCPTool, SQLiteSession
What's happening:
  • You're importing all necessary libraries.
  • The Composio and OpenAIAgentsProvider classes are imported to connect your OpenAI agent to Composio tools like Pdfmonkey.

Set up the Composio instance

load_dotenv()

api_key = os.getenv("COMPOSIO_API_KEY")
user_id = os.getenv("USER_ID")

if not api_key:
    raise RuntimeError("COMPOSIO_API_KEY is not set. Create a .env file with COMPOSIO_API_KEY=your_key")

# Initialize Composio
composio = Composio(api_key=api_key, provider=OpenAIAgentsProvider())
What's happening:
  • load_dotenv() loads your .env file so OPENAI_API_KEY and COMPOSIO_API_KEY are available as environment variables.
  • Creating a Composio instance using the API Key and OpenAIAgentsProvider class.

Create a Tool Router session

# Create a Pdfmonkey Tool Router session
session = composio.create(
    user_id=user_id,
    toolkits=["pdfmonkey"]
)

mcp_url = session.mcp.url

What is happening:

  • You give the Tool Router the user id and the toolkits you want available. Here, it is only pdfmonkey.
  • The router checks the user's Pdfmonkey connection and prepares the MCP endpoint.
  • The returned session.mcp.url is the MCP URL that your agent will use to access Pdfmonkey.
  • This approach keeps things lightweight and lets the agent request Pdfmonkey tools only when needed during the conversation.

Configure the agent

# Configure agent with MCP tool
agent = Agent(
    name="Assistant",
    model="gpt-5",
    instructions=(
        "You are a helpful assistant that can access Pdfmonkey. "
        "Help users perform Pdfmonkey operations through natural language."
    ),
    tools=[
        HostedMCPTool(
            tool_config={
                "type": "mcp",
                "server_label": "tool_router",
                "server_url": mcp_url,
                "headers": {"x-api-key": api_key},
                "require_approval": "never",
            }
        )
    ],
)
What's happening:
  • We're creating an Agent instance with a name, model (gpt-5), and clear instructions about its purpose.
  • The agent's instructions tell it that it can access Pdfmonkey and help with queries, inserts, updates, authentication, and fetching database information.
  • The tools array includes a HostedMCPTool that connects to the MCP server URL we created earlier.
  • The headers dict includes the Composio API key for secure authentication with the MCP server.
  • require_approval: 'never' means the agent can execute Pdfmonkey operations without asking for permission each time, making interactions smoother.

Start chat loop and handle conversation

print("\nComposio Tool Router session created.")

chat_session = SQLiteSession("conversation_openai_toolrouter")

print("\nChat started. Type your requests below.")
print("Commands: 'exit', 'quit', or 'q' to end\n")

async def main():
    try:
        result = await Runner.run(
            agent,
            "What can you help me with?",
            session=chat_session
        )
        print(f"Assistant: {result.final_output}\n")
    except Exception as e:
        print(f"Error: {e}\n")

    while True:
        user_input = input("You: ").strip()
        if user_input.lower() in {"exit", "quit", "q"}:
            print("Goodbye!")
            break

        result = await Runner.run(
            agent,
            user_input,
            session=chat_session
        )
        print(f"Assistant: {result.final_output}\n")

asyncio.run(main())
What's happening:
  • The program prints a session URL that you visit to authorize Pdfmonkey.
  • After authorization, the chat begins.
  • Each message you type is processed by the agent using Runner.run().
  • The responses are printed to the console, and conversations are saved locally using SQLite.
  • Typing exit, quit, or q cleanly ends the chat.

Complete Code

Here's the complete code to get you started with Pdfmonkey and open-ai-agents-sdk:

import asyncio
import os
from dotenv import load_dotenv

from composio import Composio
from composio_openai_agents import OpenAIAgentsProvider
from agents import Agent, Runner, HostedMCPTool, SQLiteSession

load_dotenv()

api_key = os.getenv("COMPOSIO_API_KEY")
user_id = os.getenv("USER_ID")

if not api_key:
    raise RuntimeError("COMPOSIO_API_KEY is not set. Create a .env file with COMPOSIO_API_KEY=your_key")

# Initialize Composio
composio = Composio(api_key=api_key, provider=OpenAIAgentsProvider())

# Create Tool Router session
session = composio.create(
    user_id=user_id,
    toolkits=["pdfmonkey"]
)
mcp_url = session.mcp.url

# Configure agent with MCP tool
agent = Agent(
    name="Assistant",
    model="gpt-5",
    instructions=(
        "You are a helpful assistant that can access Pdfmonkey. "
        "Help users perform Pdfmonkey operations through natural language."
    ),
    tools=[
        HostedMCPTool(
            tool_config={
                "type": "mcp",
                "server_label": "tool_router",
                "server_url": mcp_url,
                "headers": {"x-api-key": api_key},
                "require_approval": "never",
            }
        )
    ],
)

print("\nComposio Tool Router session created.")

chat_session = SQLiteSession("conversation_openai_toolrouter")

print("\nChat started. Type your requests below.")
print("Commands: 'exit', 'quit', or 'q' to end\n")

async def main():
    try:
        result = await Runner.run(
            agent,
            "What can you help me with?",
            session=chat_session
        )
        print(f"Assistant: {result.final_output}\n")
    except Exception as e:
        print(f"Error: {e}\n")

    while True:
        user_input = input("You: ").strip()
        if user_input.lower() in {"exit", "quit", "q"}:
            print("Goodbye!")
            break

        result = await Runner.run(
            agent,
            user_input,
            session=chat_session
        )
        print(f"Assistant: {result.final_output}\n")

asyncio.run(main())

Conclusion

This was a starter code for integrating Pdfmonkey MCP with OpenAI Agents SDK to build a functional AI agent that can interact with Pdfmonkey.

Key features:

  • Hosted MCP tool integration through Composio's Tool Router
  • SQLite session persistence for conversation history
  • Simple async chat loop for interactive testing
You can extend this by adding more toolkits, implementing custom business logic, or building a web interface around the agent.

How to build Pdfmonkey MCP Agent with another framework

Claude Agent SDK

Claude Agent SDK

Use Pdfmonkey MCP with Claude Agent SDK

Google ADK

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Use Pdfmonkey MCP with Google ADK

LangChain

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Use Pdfmonkey MCP with LangChain

Vercel AI SDK

Vercel AI SDK

Use Pdfmonkey MCP with Vercel AI SDK

Mastra AI

Mastra AI

Use Pdfmonkey MCP with Mastra AI

LlamaIndex

LlamaIndex

Use Pdfmonkey MCP with LlamaIndex

CrewAI

CrewAI

Use Pdfmonkey MCP with CrewAI

FAQ

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

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

Can I use Tool Router MCP with OpenAI Agents SDK?

Yes, you can. OpenAI Agents SDK 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 Pdfmonkey tools.

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

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

Used by agents from

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ASU
Letta
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HubSpot
Agent.ai
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DataStax
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Context
ASU
Letta
glean
HubSpot
Agent.ai
Altera
DataStax
Entelligence
Rolai

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