# How to integrate Airtable MCP with Pydantic AI

```json
{
  "title": "How to integrate Airtable MCP with Pydantic AI",
  "toolkit": "Airtable",
  "toolkit_slug": "airtable",
  "framework": "Pydantic AI",
  "framework_slug": "pydantic-ai",
  "url": "https://composio.dev/toolkits/airtable/framework/pydantic-ai",
  "markdown_url": "https://composio.dev/toolkits/airtable/framework/pydantic-ai.md",
  "updated_at": "2026-05-12T10:00:41.159Z"
}
```

## 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Airtable agent that can add new contacts from a signup list, create a project tracking table in workspace, delete outdated records from clients table through natural language commands.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.

## Also integrate Airtable with

- [ChatGPT](https://composio.dev/toolkits/airtable/framework/chatgpt)
- [OpenAI Agents SDK](https://composio.dev/toolkits/airtable/framework/open-ai-agents-sdk)
- [Claude Agent SDK](https://composio.dev/toolkits/airtable/framework/claude-agents-sdk)
- [Claude Code](https://composio.dev/toolkits/airtable/framework/claude-code)
- [Claude Cowork](https://composio.dev/toolkits/airtable/framework/claude-cowork)
- [Codex](https://composio.dev/toolkits/airtable/framework/codex)
- [Cursor](https://composio.dev/toolkits/airtable/framework/cursor)
- [VS Code](https://composio.dev/toolkits/airtable/framework/vscode)
- [OpenCode](https://composio.dev/toolkits/airtable/framework/opencode)
- [OpenClaw](https://composio.dev/toolkits/airtable/framework/openclaw)
- [Hermes](https://composio.dev/toolkits/airtable/framework/hermes-agent)
- [CLI](https://composio.dev/toolkits/airtable/framework/cli)
- [Google ADK](https://composio.dev/toolkits/airtable/framework/google-adk)
- [LangChain](https://composio.dev/toolkits/airtable/framework/langchain)
- [Vercel AI SDK](https://composio.dev/toolkits/airtable/framework/ai-sdk)
- [Mastra AI](https://composio.dev/toolkits/airtable/framework/mastra-ai)
- [LlamaIndex](https://composio.dev/toolkits/airtable/framework/llama-index)
- [CrewAI](https://composio.dev/toolkits/airtable/framework/crew-ai)

## 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 Airtable bases and tables, so your agent can create records, update fields, manage tables, retrieve schemas, and automate project tracking on your behalf.
- Seamless record creation and management: Easily instruct your agent to add new records, create multiple entries at once, or delete outdated information across any Airtable table.
- Intuitive table and field customization: Ask your agent to design new tables, add or modify fields, and tailor the structure of your bases for evolving projects and workflows.
- Efficient schema discovery: Let your agent fetch detailed schema information, including fields and configurations, to power data-driven automation and analysis.
- Collaborative commenting: Have your agent add or remove comments on specific records, making team collaboration and discussion much smoother from anywhere.
- Bulk operations for productivity: Enable your agent to perform batch actions like creating or deleting multiple records in one go, saving you time on repetitive data management tasks.

## Supported Tools

| Tool slug | Name | Description |
|---|---|---|
| `AIRTABLE_CREATE_BASE` | Create base | Creates a new Airtable base with specified tables and fields within a workspace. |
| `AIRTABLE_CREATE_COMMENT` | Create Comment | Tool to create a comment on a specific Airtable record. Use when adding comments to records, mentioning collaborators using @[userId] syntax, or creating threaded comment replies. Supports optional parentCommentId for threaded conversations. |
| `AIRTABLE_CREATE_FIELD` | Create Field | Creates a new field within a specified table in an Airtable base. |
| `AIRTABLE_CREATE_RECORD_FROM_NATURAL_LANGUAGE` | Create Record From Natural Language | Creates a new record in an Airtable table from a natural language description. Fetches the table schema, uses an LLM to generate the correct field payload, and creates the record with typecast enabled for automatic type conversion. |
| `AIRTABLE_CREATE_RECORDS` | Create records | Tool to create multiple records (up to 10) in a specified Airtable table. Use when you need to add new rows to a table with field values. Rate limit: 5 requests per second per base. |
| `AIRTABLE_CREATE_TABLE` | Create table | Creates a new table within a specified existing Airtable base, allowing definition of its name, description, and field structure. |
| `AIRTABLE_DELETE_COMMENT` | Delete Comment | Tool to delete a comment from a record in an Airtable table. Use when you need to remove an existing comment. Non-admin users can only delete their own comments; Enterprise Admins can delete any comment. |
| `AIRTABLE_DELETE_MULTIPLE_RECORDS` | Delete multiple records | Tool to delete up to 10 specified records from a table within an Airtable base. Use when you need to remove multiple records in a single operation. |
| `AIRTABLE_DELETE_RECORD` | Delete Record | Permanently deletes a specific record from an existing table within an existing Airtable base. |
| `AIRTABLE_GET_BASE_SCHEMA` | Get Base Schema | Retrieves the detailed schema for a specified Airtable base, including its tables, fields, field types, and configurations, using the `baseId`. |
| `AIRTABLE_GET_RECORD` | Get Record | Retrieves a specific record from an Airtable table by its record ID. Requires a known, valid record ID obtained from listing records or another API call - this tool cannot search or list records. Use the list records tool to find record IDs. Empty field values are not returned in the response. |
| `AIRTABLE_GET_USER_INFO` | Get user information | Retrieves information, such as ID and permission scopes, for the currently authenticated Airtable user from the `/meta/whoami` endpoint. |
| `AIRTABLE_LIST_BASES` | List bases | Retrieves all Airtable bases accessible to the authenticated user, which may include an 'offset' for pagination. |
| `AIRTABLE_LIST_COMMENTS` | List Comments | Tool to list comments on a specific Airtable record. Use when retrieving comments for a record, with optional pagination support for large comment threads. |
| `AIRTABLE_LIST_RECORDS` | List records | Tool to list records from an Airtable table with filtering, sorting, and pagination. Use when you need to retrieve multiple records from a table with optional query parameters. |
| `AIRTABLE_UPDATE_COMMENT` | Update Comment | Tool to update an existing comment on a specific Airtable record. Use when modifying comment text or updating user mentions using @[userId] syntax. API users can only update comments they have created. |
| `AIRTABLE_UPDATE_FIELD` | Update Field | Updates a field's name or description in an Airtable table. Use this action to modify field metadata without changing the field's type or options. At least one of 'name' or 'description' must be provided. |
| `AIRTABLE_UPDATE_MULTIPLE_RECORDS` | Update multiple records | Tool to update up to 10 records in an Airtable table with selective field modifications. Use when you need to modify multiple existing records or perform upsert operations. Updates are not performed atomically. |
| `AIRTABLE_UPDATE_MULTIPLE_RECORDS_PUT` | Update multiple records (PUT) | Tool to destructively update multiple records in Airtable using PUT, clearing unspecified fields. Use when you need to fully replace record data or perform upsert operations. Supports up to 10 records per request. |
| `AIRTABLE_UPDATE_RECORD` | Update record | Modifies specified fields of an existing record in an Airtable base and table; the base, table, and record must exist. |
| `AIRTABLE_UPDATE_RECORD_PUT` | Update record (PUT) | Updates an existing record in an Airtable base using PUT method. Use when you want to replace all field values, clearing any unspecified fields. For partial updates that preserve unspecified fields, use the PATCH-based update action instead. |
| `AIRTABLE_UPDATE_TABLE` | Update Table | Updates the name, description, and/or date dependency settings of a table in Airtable. Use this action to modify table metadata without changing the table's fields or views. At least one of 'name', 'description', or 'dateDependencySettings' must be provided. |
| `AIRTABLE_UPLOAD_ATTACHMENT` | Upload attachment | Uploads a file attachment to a specified field in an Airtable record. Use when you need to add a file to an attachment field. The file must be provided as a base64-encoded string. |

## Supported Triggers

| Trigger slug | Name | Description |
|---|---|---|
| `AIRTABLE_BASE_METADATA_CHANGED_TRIGGER` | Base Metadata Changed | Triggers when an existing Airtable base changes its name or permission level. |
| `AIRTABLE_BASE_SCHEMA_CHANGED_TRIGGER` | Base Schema Changed | Triggers when tables, fields, or views change in an Airtable base. |
| `AIRTABLE_USER_PROFILE_CHANGED_TRIGGER` | User Profile Changed | Triggers when the connected Airtable user's profile information changes. |
| `AIRTABLE_VIEW_CREATED_TRIGGER` | View Created | Triggers when a new view is created in an Airtable base. |
| `AIRTABLE_VIEW_DELETED_TRIGGER` | View Deleted | Triggers when a previously known Airtable view is deleted. |
| `AIRTABLE_VIEW_METADATA_CHANGED_TRIGGER` | View Metadata Changed | Triggers when an Airtable view changes its name or type. |

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

The Airtable MCP server is an implementation of the Model Context Protocol that connects your AI agent to Airtable. It provides structured and secure access so your agent can perform Airtable 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:
- Python 3.9 or higher
- A Composio account with an active API key
- Basic familiarity with Python and async programming

### 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).
- Navigate to your API settings and generate a new API key.
- Store this key securely as you'll need it for authentication.

### 2. Install dependencies

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
```bash
pip install composio pydantic-ai python-dotenv
```

### 3. Set up environment variables

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
```bash
COMPOSIO_API_KEY=your_composio_api_key_here
USER_ID=your_user_id_here
OPENAI_API_KEY=your_openai_api_key
```

### 4. Import dependencies

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
```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()
```

### 5. Create a Tool Router Session

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
```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")
```

### 6. Initialize the Pydantic AI Agent

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
```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."
    ),
)
```

### 7. Build the chat interface

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
```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()
```

### 8. Run the application

What's happening:
- The asyncio loop launches the agent and keeps it running until you exit
```python
if __name__ == "__main__":
    asyncio.run(main())
```

## Complete Code

```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.

## How to build Airtable MCP Agent with another framework

- [ChatGPT](https://composio.dev/toolkits/airtable/framework/chatgpt)
- [OpenAI Agents SDK](https://composio.dev/toolkits/airtable/framework/open-ai-agents-sdk)
- [Claude Agent SDK](https://composio.dev/toolkits/airtable/framework/claude-agents-sdk)
- [Claude Code](https://composio.dev/toolkits/airtable/framework/claude-code)
- [Claude Cowork](https://composio.dev/toolkits/airtable/framework/claude-cowork)
- [Codex](https://composio.dev/toolkits/airtable/framework/codex)
- [Cursor](https://composio.dev/toolkits/airtable/framework/cursor)
- [VS Code](https://composio.dev/toolkits/airtable/framework/vscode)
- [OpenCode](https://composio.dev/toolkits/airtable/framework/opencode)
- [OpenClaw](https://composio.dev/toolkits/airtable/framework/openclaw)
- [Hermes](https://composio.dev/toolkits/airtable/framework/hermes-agent)
- [CLI](https://composio.dev/toolkits/airtable/framework/cli)
- [Google ADK](https://composio.dev/toolkits/airtable/framework/google-adk)
- [LangChain](https://composio.dev/toolkits/airtable/framework/langchain)
- [Vercel AI SDK](https://composio.dev/toolkits/airtable/framework/ai-sdk)
- [Mastra AI](https://composio.dev/toolkits/airtable/framework/mastra-ai)
- [LlamaIndex](https://composio.dev/toolkits/airtable/framework/llama-index)
- [CrewAI](https://composio.dev/toolkits/airtable/framework/crew-ai)

## Related Toolkits

- [Google Sheets](https://composio.dev/toolkits/googlesheets) - Google Sheets is a cloud-based spreadsheet tool for real-time collaboration and data analysis. It lets teams work together from anywhere, updating information instantly.
- [Notion](https://composio.dev/toolkits/notion) - Notion is a collaborative workspace for notes, docs, wikis, and tasks. It streamlines team knowledge, project tracking, and workflow customization in one place.
- [Asana](https://composio.dev/toolkits/asana) - Asana is a collaborative work management platform for teams to organize and track projects. It streamlines teamwork, boosts productivity, and keeps everyone aligned on goals.
- [Google Tasks](https://composio.dev/toolkits/googletasks) - Google Tasks is a to-do list and task management tool integrated into Gmail and Google Calendar. It helps you organize, track, and complete tasks across your Google ecosystem.
- [Linear](https://composio.dev/toolkits/linear) - Linear is a modern issue tracking and project planning tool for fast-moving teams. It helps streamline workflows, organize projects, and boost productivity.
- [Jira](https://composio.dev/toolkits/jira) - Jira is Atlassian’s platform for bug tracking, issue tracking, and agile project management. It helps teams organize work, prioritize tasks, and deliver projects efficiently.
- [Clickup](https://composio.dev/toolkits/clickup) - ClickUp is an all-in-one productivity platform for managing tasks, docs, goals, and team collaboration. It streamlines project workflows so teams can work smarter and stay organized in one place.
- [Monday](https://composio.dev/toolkits/monday) - Monday.com is a customizable work management platform for project planning and collaboration. It helps teams organize tasks, automate workflows, and track progress in real time.
- [Addressfinder](https://composio.dev/toolkits/addressfinder) - Addressfinder is a data quality platform for verifying addresses, emails, and phone numbers. It helps you ensure accurate customer and contact data every time.
- [Agiled](https://composio.dev/toolkits/agiled) - Agiled is an all-in-one business management platform for CRM, projects, and finance. It helps you streamline workflows, consolidate client data, and manage business processes in one place.
- [Ascora](https://composio.dev/toolkits/ascora) - Ascora is a cloud-based field service management platform for service businesses. It streamlines scheduling, invoicing, and customer operations in one place.
- [Basecamp](https://composio.dev/toolkits/basecamp) - Basecamp is a project management and team collaboration tool by 37signals. It helps teams organize tasks, share files, and communicate efficiently in one place.
- [Beeminder](https://composio.dev/toolkits/beeminder) - Beeminder is an online goal-tracking platform that uses monetary pledges to keep you motivated. Stay accountable and hit your targets with real financial incentives.
- [Boxhero](https://composio.dev/toolkits/boxhero) - Boxhero is a cloud-based inventory management platform for SMBs, offering real-time updates, barcode scanning, and team collaboration. It helps businesses streamline stock tracking and analytics for smarter inventory decisions.
- [Breathe HR](https://composio.dev/toolkits/breathehr) - Breathe HR is cloud-based HR software for SMEs to manage employee data, absences, and performance. It simplifies HR admin, making it easy to keep employee records accurate and up to date.
- [Breeze](https://composio.dev/toolkits/breeze) - Breeze is a project management platform designed to help teams plan, track, and collaborate on projects. It streamlines workflows and keeps everyone on the same page.
- [Bugherd](https://composio.dev/toolkits/bugherd) - Bugherd is a visual feedback and bug tracking tool for websites. It helps teams and clients report website issues directly on live sites for faster fixes.
- [Canny](https://composio.dev/toolkits/canny) - Canny is a platform for managing customer feedback and feature requests. It helps teams prioritize product decisions based on real user insights.
- [Chmeetings](https://composio.dev/toolkits/chmeetings) - Chmeetings is a church management platform for events, members, donations, and volunteers. It streamlines church operations and improves community engagement.
- [ClickSend](https://composio.dev/toolkits/clicksend) - ClickSend is a cloud-based SMS and email marketing platform for businesses. It streamlines communication by enabling quick message delivery and contact management.

## Frequently Asked Questions

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

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

### Can I use Tool Router MCP with Pydantic AI?

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 Airtable tools.

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

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

---
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