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How to integrate Conveyor MCP with Pydantic AI

Connect Pydantic AI to Conveyor MCP. List all pending authorization requests, fetch all documents in your trust center, and more using natural language, with authentication handled for you.

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Conveyor is a platform that automates security reviews with a Trust Center and AI-driven questionnaire automation. It streamlines compliance and vendor security processes for faster, hassle-free reviews.

25 Tools

Introduction

This guide walks you through connecting Conveyor to Pydantic AI using the Composio tool router. By the end, you'll have a working Conveyor agent that can submit security questionnaires, upload Trust Center documents, manage access authorizations, browse knowledge base questions, and track interactions through natural language commands.

This guide will help you understand how to give your Pydantic AI agent real control over a Conveyor account through Composio's Conveyor MCP server.

Before we dive in, let's take a quick look at the key ideas and tools involved.

Also integrate Conveyor with

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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 Conveyor
  • 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 Conveyor 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 Conveyor MCP server, and what's possible with it?

The Conveyor MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Conveyor account. It provides structured and secure access to your security reviews and compliance workflows, so your agent can perform actions like retrieving documents, managing authorization requests, tracking connections, and automating security questionnaire processes on your behalf.

  • Authorization request management: Fetch, list, and review details of all authorization requests, making it easy for your agent to help you track and respond to security and compliance requests in real time.
  • Document and folder automation: Retrieve, organize, or delete specific documents and folders, ensuring your Trust Center stays tidy and up to date without manual effort.
  • Connection insights and tracking: Access a complete list of your Conveyor connections, letting your agent monitor integrations and stay on top of your security ecosystem.
  • Interaction history by document: Instantly pull all interactions related to a specific document, so your agent can summarize or audit user activity for compliance needs.
  • API token validation and guidance: Use AI-driven guidance to validate API tokens and get structured support for access issues, helping keep your Conveyor integration secure and running smoothly.

What is the Composio tool router, and how does it fit here?

What is Composio SDK?

Composio's Composio SDK 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 Composio SDK

The tool router generates a secure MCP URL that your agents can access to perform actions.

How the Composio SDK works

The Composio SDK 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

Step by step09 STEPS
1

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
2

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.
3

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 Conveyor
  • pydantic-ai lets you create structured AI agents with tool support
  • python-dotenv loads your environment variables securely from a .env file
4

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
5

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 Conveyor
  • MCPServerStreamableHTTP connects to the Conveyor MCP server endpoint
  • Agent from Pydantic AI lets you define and run the AI assistant
6

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 Conveyor
    composio = Composio(api_key=api_key)
    session = composio.create(
        user_id=user_id,
        toolkits=["conveyor"],
    )
    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 Conveyor 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
7

Initialize the Pydantic AI Agent

python
# Attach the MCP server to a Pydantic AI Agent
conveyor_mcp = MCPServerStreamableHTTP(url, headers={"x-api-key": COMPOSIO_API_KEY})
agent = Agent(
    "openai:gpt-5",
    toolsets=[conveyor_mcp],
    instructions=(
        "You are a Conveyor assistant. Use Conveyor tools to help users "
        "with their requests. Ask clarifying questions when needed."
    ),
)
What's happening:
  • The MCP client connects to the Conveyor endpoint
  • The agent uses GPT-5 to interpret user commands and perform Conveyor operations
  • The instructions field defines the agent's role and behavior
8

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 Conveyor.\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
  • Conveyor API calls happen automatically under the hood
  • The model keeps conversation history to maintain context across turns
9

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 Conveyor and Pydantic AI:

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()

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 Conveyor
    composio = Composio(api_key=api_key)
    session = composio.create(
        user_id=user_id,
        toolkits=["conveyor"],
    )
    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
    conveyor_mcp = MCPServerStreamableHTTP(url, headers={"x-api-key": COMPOSIO_API_KEY})
    agent = Agent(
        "openai:gpt-5",
        toolsets=[conveyor_mcp],
        instructions=(
            "You are a Conveyor assistant. Use Conveyor 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 Conveyor.\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 Conveyor through Composio's Tool Router. With this setup, your agent can perform real Conveyor 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 + Conveyor for workflow automation)
This architecture makes your AI agent "agent-native", able to securely use APIs in a unified, composable way without custom integrations.
TOOLS

Supported Tools

Every Conveyor action and event your agent gets out of the box.

Delete a Conveyor document

Tool to delete a specific document.

Delete folder

Permanently deletes a folder from the Conveyor Exchange by its UUID.

Get all access groups

Tool to retrieve all access groups for a program.

Get Authorization Request

Retrieves details of a specific authorization request from Conveyor Exchange by its ID.

Get Authorization Requests

Retrieves authorization requests from Conveyor's Trust Center.

Get authorizations

Retrieve authorizations from your Conveyor Trust Center.

Get all Conveyor connections

Retrieve a page of Conveyor connections, optionally filtered by domain.

Get all Conveyor documents

Retrieve documents and their metadata from the Conveyor Trust Center without request parameters.

List all Exchange folders

Retrieve folders from the Conveyor Exchange workspace, including IDs, names, and available timestamps and document IDs.

Get all interactions

Retrieve a page of document or Q&A interactions across Conveyor, optionally filtered by interaction type.

Get interactions by connection ID

Retrieve a page of interactions associated with a specific connection, optionally filtered by interaction type.

Get interactions by document ID

Retrieve a page of interactions associated with a specific document, optionally filtered by creation date.

Get Knowledge Base Questions

Retrieves knowledge base questions from Conveyor.

Get product lines

Retrieves all product lines configured in Conveyor.

Get questionnaires

Retrieves a page of Conveyor questionnaires with optional status, product line, and date filters.

Patch authorization

Update an existing authorization by revoking Trust Center access or changing its Access Group assignments.

Update Conveyor document

Update a Conveyor document's metadata.

Update questionnaire request

Updates a Conveyor questionnaire request by ID, including its external ID, case IDs, raw data, source system information, or file string.

Create new authorization

Tool to create a new authorization in Conveyor Exchange.

Upload new document

Tool to upload a new document to Conveyor's Knowledge Library.

Create new folder

Tool to create a new folder in Conveyor Exchange.

Submit new questionnaire

Tool to submit a new questionnaire to Conveyor.

Create questionnaire request

Tool to create a new questionnaire request in Conveyor.

Create new review

Tool to create a review in Conveyor with optional references to external VM unique IDs.

Submit single question

Submit a single question to Conveyor's AI knowledge base and receive an immediate answer.

FRAMEWORKS

How to build Conveyor MCP Agent with another framework

Claude Code logo

Claude Code

Use Conveyor MCP with Claude Code

Claude Cowork logo

Claude Cowork

Use Conveyor MCP with Claude Cowork

OpenAI Agents SDK logo

OpenAI Agents SDK

Use Conveyor MCP with OpenAI Agents SDK

Claude Agent SDK logo

Claude Agent SDK

Use Conveyor MCP with Claude Agent SDK

Codex logo

Codex

Use Conveyor MCP with Codex

OpenClaw logo

OpenClaw

Use Conveyor MCP with OpenClaw

Hermes logo

Hermes

Use Conveyor MCP with Hermes

Atomic Agent logo

Atomic Agent

Use Conveyor MCP with Atomic Agent

Google ADK logo

Google ADK

Use Conveyor MCP with Google ADK

LangChain logo

LangChain

Use Conveyor MCP with LangChain

Vercel AI SDK logo

Vercel AI SDK

Use Conveyor MCP with Vercel AI SDK

Mastra AI logo

Mastra AI

Use Conveyor MCP with Mastra AI

LlamaIndex logo

LlamaIndex

Use Conveyor MCP with LlamaIndex

CrewAI logo

CrewAI

Use Conveyor MCP with CrewAI

AutoGen logo

AutoGen

Use Conveyor MCP with AutoGen

FAQ

Frequently asked questions

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

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 Conveyor tools.

Yes, absolutely. You can configure which Conveyor 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.

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 Conveyor data and credentials are handled as safely as possible.

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