How to integrate Airparser MCP with CrewAI

This guide walks you through connecting Airparser to CrewAI 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 CrewAI 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 CrewAI 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 CrewAI 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 AILlamaIndex

TL;DR

Here's what you'll learn:
  • Get a Composio API key and configure your Airparser connection
  • Set up CrewAI with an MCP enabled agent
  • Create a Tool Router session or standalone MCP server for Airparser
  • Build a conversational loop where your agent can execute Airparser operations

What is CrewAI?

CrewAI is a powerful framework for building multi-agent AI systems. It provides primitives for defining agents with specific roles, creating tasks, and orchestrating workflows through crews.

Key features include:

  • Agent Roles: Define specialized agents with specific goals and backstories
  • Task Management: Create tasks with clear descriptions and expected outputs
  • Crew Orchestration: Combine agents and tasks into collaborative workflows
  • MCP Integration: Connect to external tools through Model Context Protocol

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 step08 STEPS
1

Prerequisites

Before starting, make sure you have:
  • Python 3.9 or higher
  • A Composio account and API key
  • A Airparser connection authorized in Composio
  • An OpenAI API key for the CrewAI LLM
  • Basic familiarity with Python
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 crewai crewai-tools[mcp] python-dotenv
What's happening:
  • composio connects your agent to Airparser via MCP
  • crewai provides Agent, Task, Crew, and LLM primitives
  • crewai-tools[mcp] includes MCP helpers
  • python-dotenv loads environment variables from .env
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_here

Create a .env file in your project root.

What's happening:

  • COMPOSIO_API_KEY authenticates with Composio
  • USER_ID scopes the session to your account
  • OPENAI_API_KEY lets CrewAI use your chosen OpenAI model
5

Import dependencies

python
import os
from composio import Composio
from crewai import Agent, Task, Crew
from crewai_tools import MCPServerAdapter
import dotenv

dotenv.load_dotenv()

COMPOSIO_API_KEY = os.getenv("COMPOSIO_API_KEY")
COMPOSIO_USER_ID = os.getenv("COMPOSIO_USER_ID")

if not COMPOSIO_API_KEY:
    raise ValueError("COMPOSIO_API_KEY is not set")
if not COMPOSIO_USER_ID:
    raise ValueError("COMPOSIO_USER_ID is not set")
What's happening:
  • CrewAI classes define agents and tasks, and run the workflow
  • MCPServerHTTP connects the agent to an MCP endpoint
  • Composio will give you a short lived Airparser MCP URL
6

Create a Composio Tool Router session for Airparser

python
composio_client = Composio(api_key=COMPOSIO_API_KEY)
session = composio_client.create(user_id=COMPOSIO_USER_ID, toolkits=["airparser"])

url = session.mcp.url
What's happening:
  • You create a Airparser only session through Composio
  • Composio returns an MCP HTTP URL that exposes Airparser tools
7

Initialize the MCP Server

python
server_params = {
    "url": url,
    "transport": "streamable-http",
    "headers": {"x-api-key": COMPOSIO_API_KEY},
}

with MCPServerAdapter(server_params) as tools:
    agent = Agent(
        role="Search Assistant",
        goal="Help users search the internet effectively",
        backstory="You are a helpful assistant with access to search tools.",
        tools=tools,
        verbose=False,
        max_iter=10,
    )
What's Happening:
  • Server Configuration: The code sets up connection parameters including the MCP server URL, streamable HTTP transport, and Composio API key authentication.
  • MCP Adapter Bridge: MCPServerAdapter acts as a context manager that converts Composio MCP tools into a CrewAI-compatible format.
  • Agent Setup: Creates a CrewAI Agent with a defined role (Search Assistant), goal (help with internet searches), and access to the MCP tools.
  • Configuration Options: The agent includes settings like verbose=False for clean output and max_iter=10 to prevent infinite loops.
  • Dynamic Tool Usage: Once created, the agent automatically accesses all Composio Search tools and decides when to use them based on user queries.
8

Create a CLI Chatloop and define the Crew

python
print("Chat started! Type 'exit' or 'quit' to end.\n")

conversation_context = ""

while True:
    user_input = input("You: ").strip()

    if user_input.lower() in ["exit", "quit", "bye"]:
        print("\nGoodbye!")
        break

    if not user_input:
        continue

    conversation_context += f"\nUser: {user_input}\n"
    print("\nAgent is thinking...\n")

    task = Task(
        description=(
            f"Conversation history:\n{conversation_context}\n\n"
            f"Current request: {user_input}"
        ),
        expected_output="A helpful response addressing the user's request",
        agent=agent,
    )

    crew = Crew(agents=[agent], tasks=[task], verbose=False)
    result = crew.kickoff()
    response = str(result)

    conversation_context += f"Agent: {response}\n"
    print(f"Agent: {response}\n")
What's Happening:
  • Interactive CLI Setup: The code creates an infinite loop that continuously prompts for user input and maintains the entire conversation history in a string variable.
  • Input Validation: Empty inputs are ignored to prevent processing blank messages and keep the conversation clean.
  • Context Building: Each user message is appended to the conversation context, which preserves the full dialogue history for better agent responses.
  • Dynamic Task Creation: For every user input, a new Task is created that includes both the full conversation history and the current request as context.
  • Crew Execution: A Crew is instantiated with the agent and task, then kicked off to process the request and generate a response.
  • Response Management: The agent's response is converted to a string, added to the conversation context, and displayed to the user, maintaining conversational continuity.

Complete Code

Here's the complete code to get you started with Airparser and CrewAI:

python
from crewai import Agent, Task, Crew, LLM
from crewai_tools import MCPServerAdapter
from composio import Composio
from dotenv import load_dotenv
import os

load_dotenv()

GOOGLE_API_KEY = os.getenv("GOOGLE_API_KEY")
COMPOSIO_API_KEY = os.getenv("COMPOSIO_API_KEY")
COMPOSIO_USER_ID = os.getenv("COMPOSIO_USER_ID")

if not GOOGLE_API_KEY:
    raise ValueError("GOOGLE_API_KEY is not set in the environment.")
if not COMPOSIO_API_KEY:
    raise ValueError("COMPOSIO_API_KEY is not set in the environment.")
if not COMPOSIO_USER_ID:
    raise ValueError("COMPOSIO_USER_ID is not set in the environment.")

# Initialize Composio and create a session
composio = Composio(api_key=COMPOSIO_API_KEY)
session = composio.create(
    user_id=COMPOSIO_USER_ID,
    toolkits=["airparser"],
)
url = session.mcp.url

# Configure LLM
llm = LLM(
    model="gpt-5",
    api_key=os.getenv("OPENAI_API_KEY"),
)

server_params = {
    "url": url,
    "transport": "streamable-http",
    "headers": {"x-api-key": COMPOSIO_API_KEY},
}

with MCPServerAdapter(server_params) as tools:
    agent = Agent(
        role="Search Assistant",
        goal="Help users with internet searches",
        backstory="You are an expert assistant with access to Composio Search tools.",
        tools=tools,
        llm=llm,
        verbose=False,
        max_iter=10,
    )

    print("Chat started! Type 'exit' or 'quit' to end.\n")

    conversation_context = ""

    while True:
        user_input = input("You: ").strip()

        if user_input.lower() in ["exit", "quit", "bye"]:
            print("\nGoodbye!")
            break

        if not user_input:
            continue

        conversation_context += f"\nUser: {user_input}\n"
        print("\nAgent is thinking...\n")

        task = Task(
            description=(
                f"Conversation history:\n{conversation_context}\n\n"
                f"Current request: {user_input}"
            ),
            expected_output="A helpful response addressing the user's request",
            agent=agent,
        )

        crew = Crew(agents=[agent], tasks=[task], verbose=False)
        result = crew.kickoff()
        response = str(result)

        conversation_context += f"Agent: {response}\n"
        print(f"Agent: {response}\n")

Conclusion

You now have a CrewAI agent connected to Airparser through Composio's Tool Router. The agent can perform Airparser operations through natural language commands.

Next steps:

  • Add role-specific instructions to customize agent behavior
  • Plug in more toolkits for multi-app workflows
  • Chain tasks for complex multi-step operations
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

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. CrewAI 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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