# How to integrate Pandadoc MCP with OpenAI Agents SDK

```json
{
  "title": "How to integrate Pandadoc MCP with OpenAI Agents SDK",
  "toolkit": "Pandadoc",
  "toolkit_slug": "pandadoc",
  "framework": "OpenAI Agents SDK",
  "framework_slug": "open-ai-agents-sdk",
  "url": "https://composio.dev/toolkits/pandadoc/framework/open-ai-agents-sdk",
  "markdown_url": "https://composio.dev/toolkits/pandadoc/framework/open-ai-agents-sdk.md",
  "updated_at": "2026-05-12T10:21:13.594Z"
}
```

## Introduction

This guide walks you through connecting Pandadoc to the OpenAI Agents SDK using the Composio tool router. By the end, you'll have a working Pandadoc agent that can create a new contract from pdf upload, add an attachment to a draft proposal, list details of your latest templates through natural language commands.
This guide will help you understand how to give your OpenAI Agents SDK agent real control over a Pandadoc account through Composio's Pandadoc MCP server.
Before we dive in, let's take a quick look at the key ideas and tools involved.

## Also integrate Pandadoc with

- [Claude Agent SDK](https://composio.dev/toolkits/pandadoc/framework/claude-agents-sdk)
- [Claude Code](https://composio.dev/toolkits/pandadoc/framework/claude-code)
- [Claude Cowork](https://composio.dev/toolkits/pandadoc/framework/claude-cowork)
- [Codex](https://composio.dev/toolkits/pandadoc/framework/codex)
- [OpenClaw](https://composio.dev/toolkits/pandadoc/framework/openclaw)
- [Hermes](https://composio.dev/toolkits/pandadoc/framework/hermes-agent)
- [CLI](https://composio.dev/toolkits/pandadoc/framework/cli)
- [Google ADK](https://composio.dev/toolkits/pandadoc/framework/google-adk)
- [LangChain](https://composio.dev/toolkits/pandadoc/framework/langchain)
- [Vercel AI SDK](https://composio.dev/toolkits/pandadoc/framework/ai-sdk)
- [Mastra AI](https://composio.dev/toolkits/pandadoc/framework/mastra-ai)
- [LlamaIndex](https://composio.dev/toolkits/pandadoc/framework/llama-index)
- [CrewAI](https://composio.dev/toolkits/pandadoc/framework/crew-ai)

## 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 Pandadoc
- Configure an AI agent that can use Pandadoc as a tool
- Run a live chat session where you can ask the agent to perform Pandadoc operations

## What is OpenAI 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 Pandadoc MCP server, and what's possible with it?

The Pandadoc MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Pandadoc account. It provides structured and secure access to your documents, templates, contacts, and workflows, so your agent can perform actions like creating documents, managing templates, organizing folders, and handling contacts on your behalf.
- Automated document creation and uploads: Have your agent generate new contracts, proposals, or agreements by uploading files or leveraging templates—ready for processing and e-signature in Pandadoc.
- Template management and customization: Let your agent create, update, or delete templates, making it easy to standardize and scale your document workflows across teams.
- Contact creation and maintenance: Seamlessly add, update, or delete contacts in your Pandadoc account, ensuring your address book stays organized and always up to date.
- Folder and document organization: Ask your agent to create structured folders, move documents, or attach supplemental files to keep your workspace tidy and accessible.
- Webhook setup for workflow automation: Empower your agent to create Pandadoc webhooks, so you can receive instant notifications about document status changes, completions, or updates—no manual checking required.

## Supported Tools

| Tool slug | Name | Description |
|---|---|---|
| `PANDADOC_CREATE_DOCUMENT_ATTACHMENT` | Create Document Attachment | Creates and adds an attachment to a PandaDoc document. This tool allows you to attach downloadable files such as supplemental materials, Excel spreadsheets, or other content without embedding them directly into the document. Attachments can be added only to documents in 'document.draft' status, with a maximum of 10 files per document and a size limit of 50MB per file. |
| `PANDADOC_CREATE_DOCUMENT_FROM_FILE` | Create Document from File Upload | Creates a new document in PandaDoc by uploading a file (PDF, DOCX, or RTF). Converts existing documents into PandaDoc documents for processing, signing, and tracking. Either `file` or `url` must be provided; omitting both will fail. Large files may time out during upload and conversion. |
| `PANDADOC_CREATE_FOLDER` | Create Document Folder | Creates a new folder in PandaDoc to organize documents. This action allows users to create a new folder with a specified name and optionally set a parent folder to create a nested folder structure. |
| `PANDADOC_CREATE_OR_UPDATE_CONTACT` | Create or Update Contact | This tool creates a new contact or updates an existing one in PandaDoc based on the email address. If a contact with the provided email exists, it will be updated; otherwise, a new contact will be created. |
| `PANDADOC_CREATE_TEMPLATE` | Create Template | This tool allows users to create a new template in PandaDoc from a PDF file or from scratch. It handles file upload validation, parameter checks, proper error handling, and authentication with the PandaDoc API. The template can be created either by uploading a PDF file or by providing a structured content object that defines the template layout and elements. |
| `PANDADOC_CREATE_WEBHOOK` | Create PandaDoc Webhook | Creates a new webhook subscription in PandaDoc to receive notifications about specific events. This action allows you to set up webhook notifications for various document-related events such as status changes, recipient completions, and updates. The webhook will send HTTP notifications to your specified endpoint when the configured events occur. |
| `PANDADOC_DELETE_CONTACT` | Delete Contact | This tool allows you to delete a contact from your PandaDoc account. The action is permanent and cannot be undone. |
| `PANDADOC_DELETE_TEMPLATE` | Delete Template | This tool deletes a specific template from PandaDoc. Once a template is deleted, it cannot be recovered. This action is permanent and should be used with caution. |
| `PANDADOC_GET_DOCUMENT_DETAILS` | Get Document Details | Fetch detailed metadata for a specific PandaDoc document including recipients, fields/tokens values, pricing data, metadata, tags, and content-block names. Use this after discovering a document via list/search to inspect recipients/status/fields/metadata/content-block references for follow-up automation or reporting. |
| `PANDADOC_GET_TEMPLATE_DETAILS` | Get Template Details | This tool retrieves detailed information about a specific template by its ID. The endpoint returns comprehensive template details including metadata, content details, and sharing settings. |
| `PANDADOC_LIST_CONTACTS` | List Contacts | List all contacts in your PandaDoc workspace. Returns all contacts with their details including email, name, company, and contact information. Optionally filter by exact email address. Note: The API returns all contacts at once without pagination - filtering and pagination should be done client-side if needed. |
| `PANDADOC_LIST_DOCUMENT_FOLDERS` | List Document Folders | This tool retrieves a list of all document folders in PandaDoc. It's a standalone action that doesn't require any external dependencies or resource IDs. The tool will return a list of folders containing documents, with each folder containing information about its ID, name, and parent folder relationship. |
| `PANDADOC_LIST_TEMPLATES` | List Templates | This tool retrieves a list of all templates available in the PandaDoc account. It supports parameters to filter templates by name, shared status, deleted status, pagination, and tag filtering, and returns detailed template information. |
| `PANDADOC_MOVE_DOCUMENT_TO_FOLDER` | Move Document to Folder | This tool allows users to move a document to a specific folder within their PandaDoc account. It performs a POST request to move the document to the specified folder. Both the document and the destination folder must exist. Only documents in draft status can be moved; attempting to move documents in sent, completed, or other non-draft states will fail. |

## Supported Triggers

None listed.

## Creating MCP Server - Stand-alone vs Composio SDK

The Pandadoc MCP server is an implementation of the Model Context Protocol that connects your AI agent to Pandadoc. It provides structured and secure access so your agent can perform Pandadoc operations on your behalf through a secure, permission-based interface.
With Composio's managed implementation, you don't have to create your own developer app. For production, if you're building an end product, we recommend using your own credentials. The managed server helps you prototype fast and go from 0-1 faster.

## Step-by-step Guide

### 1. Prerequisites

Before starting, make sure you have:
- Composio API Key and OpenAI API Key
- Primary know-how of OpenAI Agents SDK
- A live Pandadoc project
- Some knowledge of Python or Typescript

### 1. Getting API Keys for OpenAI and Composio

OpenAI API Key
- Go to the [OpenAI dashboard](https://platform.openai.com/settings/organization/api-keys) 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](https://dashboard.composio.dev?utm_source=toolkits&utm_medium=framework_docs).
- Go to Settings and copy your API key.

### 2. Install dependencies

Install the Composio SDK and the OpenAI Agents SDK.
```python
pip install composio_openai_agents openai-agents python-dotenv
```

```typescript
npm install @composio/openai-agents @openai/agents dotenv
```

### 3. Set up environment variables

Create a .env file and add your OpenAI and Composio API keys.
```bash
OPENAI_API_KEY=sk-...your-api-key
COMPOSIO_API_KEY=your-api-key
USER_ID=composio_user@gmail.com
```

### 4. Import dependencies

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 Pandadoc.
```python
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
```

```typescript
import 'dotenv/config';
import { Composio } from '@composio/core';
import { OpenAIAgentsProvider } from '@composio/openai-agents';
import { Agent, hostedMcpTool, run, OpenAIConversationsSession } from '@openai/agents';
import * as readline from 'readline';
```

### 5. Set up the Composio instance

No description provided.
```python
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())
```

```typescript
dotenv.config();

const composioApiKey = process.env.COMPOSIO_API_KEY;
const userId = process.env.USER_ID;

if (!composioApiKey) {
  throw new Error('COMPOSIO_API_KEY is not set. Create a .env file with COMPOSIO_API_KEY=your_key');
}
if (!userId) {
  throw new Error('USER_ID is not set');
}

// Initialize Composio
const composio = new Composio({
  apiKey: composioApiKey,
  provider: new OpenAIAgentsProvider(),
});
```

### 6. Create a Tool Router session

What is happening:
- You give the Tool Router the user id and the toolkits you want available. Here, it is only pandadoc.
- The router checks the user's Pandadoc connection and prepares the MCP endpoint.
- The returned session.mcp.url is the MCP URL that your agent will use to access Pandadoc.
- This approach keeps things lightweight and lets the agent request Pandadoc tools only when needed during the conversation.
```python
# Create a Pandadoc Tool Router session
session = composio.create(
    user_id=user_id,
    toolkits=["pandadoc"]
)

mcp_url = session.mcp.url
```

```typescript
// Create Tool Router session for Pandadoc
const session = await composio.create(userId as string, {
  toolkits: ['pandadoc'],
});
const mcpUrl = session.mcp.url;
```

### 7. Configure the agent

No description provided.
```python
# Configure agent with MCP tool
agent = Agent(
    name="Assistant",
    model="gpt-5",
    instructions=(
        "You are a helpful assistant that can access Pandadoc. "
        "Help users perform Pandadoc 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",
            }
        )
    ],
)
```

```typescript
// Configure agent with MCP tool
const agent = new Agent({
  name: 'Assistant',
  model: 'gpt-5',
  instructions:
    'You are a helpful assistant that can access Pandadoc. Help users perform Pandadoc operations through natural language.',
  tools: [
    hostedMcpTool({
      serverLabel: 'tool_router',
      serverUrl: mcpUrl,
      headers: { 'x-api-key': composioApiKey },
      requireApproval: 'never',
    }),
  ],
});
```

### 8. Start chat loop and handle conversation

No description provided.
```python
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())
```

```typescript
// Keep conversation state across turns
const conversationSession = new OpenAIConversationsSession();

// Simple CLI
const rl = readline.createInterface({
  input: process.stdin,
  output: process.stdout,
  prompt: 'You: ',
});

console.log('\nComposio Tool Router session created.');
console.log('\nChat started. Type your requests below.');
console.log("Commands: 'exit', 'quit', or 'q' to end\n");

try {
  const first = await run(agent, 'What can you help me with?', { session: conversationSession });
  console.log(`Assistant: ${first.finalOutput}\n`);
} catch (e) {
  console.error('Error:', e instanceof Error ? e.message : e, '\n');
}

rl.prompt();

rl.on('line', async (userInput) => {
  const text = userInput.trim();

  if (['exit', 'quit', 'q'].includes(text.toLowerCase())) {
    console.log('Goodbye!');
    rl.close();
    process.exit(0);
  }

  if (!text) {
    rl.prompt();
    return;
  }

  try {
    const result = await run(agent, text, { session: conversationSession });
    console.log(`\nAssistant: ${result.finalOutput}\n`);
  } catch (e) {
    console.error('Error:', e instanceof Error ? e.message : e, '\n');
  }

  rl.prompt();
});

rl.on('close', () => {
  console.log('\n👋 Session ended.');
  process.exit(0);
});
```

## Complete Code

```python
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=["pandadoc"]
)
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 Pandadoc. "
        "Help users perform Pandadoc 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())
```

```typescript
import 'dotenv/config';
import { Composio } from '@composio/core';
import { OpenAIAgentsProvider } from '@composio/openai-agents';
import { Agent, hostedMcpTool, run, OpenAIConversationsSession } from '@openai/agents';
import * as readline from 'readline';

const composioApiKey = process.env.COMPOSIO_API_KEY;
const userId = process.env.USER_ID;

if (!composioApiKey) {
  throw new Error('COMPOSIO_API_KEY is not set. Create a .env file with COMPOSIO_API_KEY=your_key');
}
if (!userId) {
  throw new Error('USER_ID is not set');
}

// Initialize Composio
const composio = new Composio({
  apiKey: composioApiKey,
  provider: new OpenAIAgentsProvider(),
});

async function main() {
  // Create Tool Router session
  const session = await composio.create(userId as string, {
    toolkits: ['pandadoc'],
  });
  const mcpUrl = session.mcp.url;

  // Configure agent with MCP tool
  const agent = new Agent({
    name: 'Assistant',
    model: 'gpt-5',
    instructions:
      'You are a helpful assistant that can access Pandadoc. Help users perform Pandadoc operations through natural language.',
    tools: [
      hostedMcpTool({
        serverLabel: 'tool_router',
        serverUrl: mcpUrl,
        headers: { 'x-api-key': composioApiKey },
        requireApproval: 'never',
      }),
    ],
  });

  // Keep conversation state across turns
  const conversationSession = new OpenAIConversationsSession();

  // Simple CLI
  const rl = readline.createInterface({
    input: process.stdin,
    output: process.stdout,
    prompt: 'You: ',
  });

  console.log('\nComposio Tool Router session created.');
  console.log('\nChat started. Type your requests below.');
  console.log("Commands: 'exit', 'quit', or 'q' to end\n");

  try {
    const first = await run(agent, 'What can you help me with?', { session: conversationSession });
    console.log(`Assistant: ${first.finalOutput}\n`);
  } catch (e) {
    console.error('Error:', e instanceof Error ? e.message : e, '\n');
  }

  rl.prompt();

  rl.on('line', async (userInput) => {
    const text = userInput.trim();

    if (['exit', 'quit', 'q'].includes(text.toLowerCase())) {
      console.log('Goodbye!');
      rl.close();
      process.exit(0);
    }

    if (!text) {
      rl.prompt();
      return;
    }

    try {
      const result = await run(agent, text, { session: conversationSession });
      console.log(`\nAssistant: ${result.finalOutput}\n`);
    } catch (e) {
      console.error('Error:', e instanceof Error ? e.message : e, '\n');
    }

    rl.prompt();
  });

  rl.on('close', () => {
    console.log('\nSession ended.');
    process.exit(0);
  });
}

main().catch((err) => {
  console.error('Fatal error:', err);
  process.exit(1);
});
```

## Conclusion

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

- [Claude Agent SDK](https://composio.dev/toolkits/pandadoc/framework/claude-agents-sdk)
- [Claude Code](https://composio.dev/toolkits/pandadoc/framework/claude-code)
- [Claude Cowork](https://composio.dev/toolkits/pandadoc/framework/claude-cowork)
- [Codex](https://composio.dev/toolkits/pandadoc/framework/codex)
- [OpenClaw](https://composio.dev/toolkits/pandadoc/framework/openclaw)
- [Hermes](https://composio.dev/toolkits/pandadoc/framework/hermes-agent)
- [CLI](https://composio.dev/toolkits/pandadoc/framework/cli)
- [Google ADK](https://composio.dev/toolkits/pandadoc/framework/google-adk)
- [LangChain](https://composio.dev/toolkits/pandadoc/framework/langchain)
- [Vercel AI SDK](https://composio.dev/toolkits/pandadoc/framework/ai-sdk)
- [Mastra AI](https://composio.dev/toolkits/pandadoc/framework/mastra-ai)
- [LlamaIndex](https://composio.dev/toolkits/pandadoc/framework/llama-index)
- [CrewAI](https://composio.dev/toolkits/pandadoc/framework/crew-ai)

## Related Toolkits

- [Google Drive](https://composio.dev/toolkits/googledrive) - Google Drive is a cloud storage platform for uploading, sharing, and collaborating on files. It's perfect for keeping your documents accessible and organized across devices.
- [Google Docs](https://composio.dev/toolkits/googledocs) - Google Docs is a cloud-based word processor that enables document creation and real-time collaboration. Its seamless sharing and version history make team editing and content management a breeze.
- [Google Super](https://composio.dev/toolkits/googlesuper) - Google Super is an all-in-one suite combining Gmail, Drive, Calendar, Sheets, Analytics, and more. It gives you a unified platform to manage your digital life, boosting productivity and organization.
- [Affinda](https://composio.dev/toolkits/affinda) - Affinda is an AI-powered document processing platform that automates data extraction from resumes, invoices, and more. It streamlines document-heavy workflows by turning files into structured, actionable data.
- [Agility cms](https://composio.dev/toolkits/agility_cms) - Agility CMS is a headless content management system for building and managing digital experiences across platforms. It lets teams update content quickly and deliver omnichannel experiences with ease.
- [Algodocs](https://composio.dev/toolkits/algodocs) - Algodocs is an AI-powered platform that automates data extraction from business documents. It delivers fast, secure, and accurate processing without templates or manual training.
- [Api2pdf](https://composio.dev/toolkits/api2pdf) - Api2Pdf is a REST API for generating PDFs from HTML, URLs, and documents using powerful engines like wkhtmltopdf and Headless Chrome. It streamlines document conversion and automation for developers and businesses.
- [Aryn](https://composio.dev/toolkits/aryn) - Aryn is an AI-powered platform for parsing, extracting, and analyzing data from unstructured documents. Use it to automate document processing and unlock actionable insights from your files.
- [Boldsign](https://composio.dev/toolkits/boldsign) - Boldsign is a digital eSignature platform for sending, signing, and tracking documents online. Organizations use it to automate agreements and manage legally binding workflows efficiently.
- [Boloforms](https://composio.dev/toolkits/boloforms) - BoloForms is an eSignature platform built for small businesses, offering unlimited signatures, templates, and forms. It simplifies digital document signing and team collaboration at a predictable, fixed price.
- [Box](https://composio.dev/toolkits/box) - Box is a cloud content management and file sharing platform for businesses. It helps teams securely store, organize, and collaborate on files from anywhere.
- [Carbone](https://composio.dev/toolkits/carbone) - Carbone is a blazing-fast report generator that turns JSON data into PDFs, Word docs, spreadsheets, and more using flexible templates. It lets you automate document creation at scale with minimal code.
- [Castingwords](https://composio.dev/toolkits/castingwords) - CastingWords is a transcription service specializing in human-powered, accurate transcripts via a simple API. Get seamless audio-to-text conversion for interviews, meetings, podcasts, and more.
- [Cloudconvert](https://composio.dev/toolkits/cloudconvert) - CloudConvert is a powerful file conversion service supporting over 200 file formats. It streamlines converting, compressing, and managing documents, media, and more, all in one place.
- [Cloudlayer](https://composio.dev/toolkits/cloudlayer) - Cloudlayer is a document and asset generation service for creating PDFs and images via API or SDKs. It lets you automate high-quality doc creation, saving dev time and reducing manual work.
- [Cloudpress](https://composio.dev/toolkits/cloudpress) - Cloudpress is a content export tool for Google Docs and Notion. It automates publishing to your favorite Content Management Systems.
- [Contentful graphql](https://composio.dev/toolkits/contentful_graphql) - Contentful graphql is a content delivery API that lets you access Contentful data using GraphQL queries. It gives you efficient, flexible ways to fetch and manage structured content for any digital project.
- [Conversion tools](https://composio.dev/toolkits/conversion_tools) - Conversion Tools is an online service for converting documents between formats such as PDF, Word, Excel, XML, and CSV. It lets you automate complex document workflows with just a few clicks.
- [Convertapi](https://composio.dev/toolkits/convertapi) - ConvertAPI is a robust file conversion service for documents, images, and spreadsheets. It streamlines programmatic format changes and lets developers automate complex workflows with a single API.
- [Craftmypdf](https://composio.dev/toolkits/craftmypdf) - CraftMyPDF is a web-based service for designing and generating PDFs with templates and live data. It streamlines document creation by automating personalized PDFs at scale.

## Frequently Asked Questions

### What are the differences in Tool Router MCP and Pandadoc MCP?

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

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

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

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[See all toolkits](https://composio.dev/toolkits) · [Composio docs](https://docs.composio.dev/llms.txt)