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How to integrate Airtable MCP with Pydantic AI

Connect Pydantic AI to Airtable MCP. Add new contacts from a signup list, create a project tracking table in workspace, and more using natural language, with authentication handled for you.

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Airtable combines the flexibility of spreadsheets with the power of a database for easy project and data management. Teams use Airtable to organize, track, and collaborate with custom views and automations.

26 Tools6 Triggers

Introduction

This guide walks you through connecting Airtable to Pydantic AI using the Composio tool router. By the end, you'll have a working Airtable agent that can create and update records, build bases and tables, manage fields, add comments, and upload attachments through natural language commands.

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

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

For exact tool slugs and parameter tables, see the tool catalog in the Composio docs.

Also integrate Airtable with

Claude CodeCursorChatGPTClaude CoworkAntigravityOpenAI Agents SDKClaude Agent SDKGrok BotMuseCodexGrok BuildVS CodeOpenCodeOpenClawHermesAtomic AgentGoogle ADKLangChainVercel AI SDKMastra AILlamaIndexCrewAIAutoGen

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

The Airtable MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Airtable account. It provides structured and secure access to your bases, table structures, records, and account details, so your agent can inspect bases, retrieve records, create entries, and update data on your behalf.

  • Base discovery: Have your agent list the Airtable bases available to your connected account.
  • Table and field inspection: Let the agent review a base’s tables, fields, field types, and configurations before working with its data.
  • Record retrieval: Direct your agent to fetch a specific record when you need its latest field values.
  • Natural language record creation: Instruct your agent to turn a plain-language request into a new Airtable record or create several records at once.
  • Targeted record updates: Have your agent modify selected fields in one or several existing records while preserving the remaining data.

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

Supported Tools and Triggers

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

Create base

Creates a new Airtable base with specified tables and fields within a workspace.

Create Comment

Tool to create a comment on a specific Airtable record.

Create Field

Creates a new field within a specified table in an Airtable base.

Create Record From Natural Language

Creates an Airtable record from a natural language description using the table schema and an LLM-generated field payload.

Create records

Tool to create multiple records (up to 10) in a specified Airtable table.

Create table

Creates a table in an existing Airtable base with a specified name, description, and field structure.

Delete Comment

Tool to delete a comment from a record in an Airtable table.

Delete multiple records

Tool to delete up to 10 specified records from a table within an Airtable base.

Delete Record

Permanently deletes a specific record from an existing table within an existing Airtable base.

Get Attachment Info

Retrieves attachment metadata and download URLs from a specified Airtable record field, including sizes, types, and thumbnails when available; attachment URLs expire 2 hours after retrieval.

Get Base Metadata

Retrieves basic metadata for one Airtable base.

Get Base Schema

Retrieves the detailed schema for a specified Airtable base, including its tables, fields, field types, and configurations, using the `baseId`.

Get Record

Retrieves a specific record from an Airtable table by its record ID.

Get user information

Retrieves information, such as ID and permission scopes, for the currently authenticated Airtable user from the `/meta/whoami` endpoint.

List bases

Lists a page of Airtable bases accessible to the authenticated token.

List Comments

Tool to list comments on a specific Airtable record.

List records

Tool to list records from an Airtable table with filtering, sorting, and pagination.

Update Comment

Tool to update an existing comment on a specific Airtable record.

Update Field

Updates a field's name or description in an Airtable table.

Update multiple records

Tool to update up to 10 records in an Airtable table with selective field modifications.

Update multiple records (PUT)

Tool to destructively update multiple records in Airtable using PUT, clearing unspecified fields.

Update record

Modifies specified fields of an existing record in an Airtable base and table; the base, table, and record must exist.

Update record (PUT)

Updates an existing record in an Airtable base using PUT method.

Update Table

Updates the name, description, and/or date dependency settings of a table in Airtable.

Upload attachment

Uploads a file attachment to a specified field in an Airtable record.

Who Am I

Returns the connected Airtable account's user ID and, when available, email address.

FRAMEWORKS

How to build Airtable MCP Agent with another framework

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Antigravity logo

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OpenAI Agents SDK logo

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Claude Agent SDK logo

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Grok Bot logo

Grok Bot

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Muse logo

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Codex logo

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Grok Build logo

Grok Build

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VS Code logo

VS Code

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OpenCode logo

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OpenClaw logo

OpenClaw

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Hermes logo

Hermes

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Atomic Agent logo

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Google ADK logo

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LangChain logo

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Vercel AI SDK logo

Vercel AI SDK

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Mastra AI logo

Mastra AI

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LlamaIndex logo

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CrewAI logo

CrewAI

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AutoGen logo

AutoGen

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FAQ

Frequently asked questions

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