How to integrate Airparser MCP with Pydantic AI

This guide walks you through connecting Airparser to Pydantic AI using the Composio tool router. By the end, you'll have a working Airparser agent that can extract invoice data from supplier emails, parse resumes into structured candidate records, export receipt fields from uploaded pdfs through natural language commands. This guide will help you understand how to give your Pydantic AI agent real control over a Airparser account through Composio's Airparser MCP server. Before we dive in, let's take a quick look at the key ideas and tools involved.

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Airparser is an AI-powered parser for extracting structured data from emails, documents, PDFs, and files. It turns messy inbound content into clean fields your apps and workflows can use.

10 Tools

Introduction

This guide walks you through connecting Airparser to Pydantic AI using the Composio tool router. By the end, you'll have a working Airparser agent that can extract invoice data from supplier emails, parse resumes into structured candidate records, export receipt fields from uploaded pdfs through natural language commands.

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

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

Also integrate Airparser with

OpenAI Agents SDKClaude Agent SDKClaude CodeClaude CoworkCodexOpenClawHermesCLIGoogle ADKLangChainVercel AI SDKMastra AILlamaIndexCrewAI

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

The Airparser MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Airparser account. It provides structured and secure access to your document inboxes, extraction schemas, parsed documents, and account usage, so your agent can create parsers, extract structured data, review documents, manage schemas, and monitor parsing credits on your behalf.

  • Document parsing and extraction: Have your agent upload a document to an inbox, wait for processing, and return the extracted data as structured JSON.
  • Inbox and parser management: Let the agent create inboxes for document families, review their parsing settings, list available inboxes, or delete inboxes you no longer need.
  • Extraction schema control: Direct your agent to create or replace an inbox's extraction schema, or copy a working schema from one inbox to another.
  • Parsed document review: Instruct your agent to list or search documents in an inbox, then retrieve a document's parsing status and extracted output.
  • Account usage monitoring: Have your agent check your Airparser plan, account verification state, and parsing credit usage.

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 Airparser
  • 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 Airparser
  • MCPServerStreamableHTTP connects to the Airparser 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 Airparser
    composio = Composio(api_key=api_key)
    session = composio.create(
        user_id=user_id,
        toolkits=["airparser"],
    )
    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 Airparser 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
airparser_mcp = MCPServerStreamableHTTP(url, headers={"x-api-key": COMPOSIO_API_KEY})
agent = Agent(
    "openai:gpt-5",
    toolsets=[airparser_mcp],
    instructions=(
        "You are a Airparser assistant. Use Airparser tools to help users "
        "with their requests. Ask clarifying questions when needed."
    ),
)
What's happening:
  • The MCP client connects to the Airparser endpoint
  • The agent uses GPT-5 to interpret user commands and perform Airparser 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 Airparser.\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
  • Airparser 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 Airparser 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 Airparser
    composio = Composio(api_key=api_key)
    session = composio.create(
        user_id=user_id,
        toolkits=["airparser"],
    )
    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
    airparser_mcp = MCPServerStreamableHTTP(url, headers={"x-api-key": COMPOSIO_API_KEY})
    agent = Agent(
        "openai:gpt-5",
        toolsets=[airparser_mcp],
        instructions=(
            "You are a Airparser assistant. Use Airparser 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 Airparser.\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 Airparser through Composio's Tool Router. With this setup, your agent can perform real Airparser 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 + Airparser 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 Airparser action and event your agent gets out of the box.

Clone Extraction Schema

Replace a destination inbox's extraction schema with a copy of a source inbox's schema.

Create Inbox

Create an Airparser inbox/parser for a document family using a text or vision extraction engine.

Delete Inbox

Permanently delete an Airparser inbox and every document it contains.

Get Account Usage

Return the connected Airparser account's role, verification state, plan, and parsing-credit usage without exposing personal, secret, or internal account fields.

Get Document

Get parsed output and status for a document, optionally including extended source and navigation details.

Get Inbox

Get one Airparser inbox, including its current extraction schema and useful parsing configuration.

List Documents

List and search documents in one inbox, one page at a time.

List Inboxes

List the Airparser inboxes available to the connected account.

Parse Document Synchronously

Upload one document to an inbox and wait for its extracted JSON.

Upsert Extraction Schema

Create or completely replace the extraction schema used by an Airparser inbox.

FRAMEWORKS

How to build Airparser MCP Agent with another framework

OpenAI Agents SDK logo

OpenAI Agents SDK

Use Airparser MCP with OpenAI Agents SDK

Claude Agent SDK logo

Claude Agent SDK

Use Airparser MCP with Claude Agent SDK

Claude Code logo

Claude Code

Use Airparser MCP with Claude Code

Claude Cowork logo

Claude Cowork

Use Airparser MCP with Claude Cowork

Codex logo

Codex

Use Airparser MCP with Codex

OpenClaw logo

OpenClaw

Use Airparser MCP with OpenClaw

Hermes logo

Hermes

Use Airparser MCP with Hermes

CLI logo

CLI

Use Airparser MCP with CLI

Google ADK logo

Google ADK

Use Airparser MCP with Google ADK

LangChain logo

LangChain

Use Airparser MCP with LangChain

Vercel AI SDK logo

Vercel AI SDK

Use Airparser MCP with Vercel AI SDK

Mastra AI logo

Mastra AI

Use Airparser MCP with Mastra AI

LlamaIndex logo

LlamaIndex

Use Airparser MCP with LlamaIndex

CrewAI logo

CrewAI

Use Airparser MCP with CrewAI

FAQ

Frequently asked questions

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

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

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