Airtable MCP Server Oauth

🚀 Airtable OAuth MCP Server

A production-ready Model Context Protocol (MCP) server for Airtable with secure OAuth 2.0 authentication. This server enables AI assistants and applications to interact with Airtable bases through a standardized MCP interface, providing complete API coverage for all Airtable operations.

🚀 Quick Start

1. Installation

Clone the repository and install dependencies:

git clone https://github.com/onimsha/airtable-mcp-server-oauth.git
cd airtable-mcp-server-oauth
uv sync

2. Airtable OAuth Setup

1. Create an Airtable OAuth Application:
   • Visit Airtable Developer Hub.
   • Create a new OAuth integration.
   • Note your Client ID and Client Secret.
   • Set redirect URI to http://localhost:8000/oauth/callback.

3. Environment Configuration

Copy the environment template and configure your credentials:

cp .env.example .env

Edit .env with your values:

# Airtable OAuth Configuration
AIRTABLE_CLIENT_ID="your_airtable_client_id_here"
AIRTABLE_CLIENT_SECRET="your_airtable_client_secret_here"
AIRTABLE_REDIRECT_URI="http://localhost:8000/oauth/callback"

# Server Configuration
HOST="0.0.0.0"
PORT=8000
LOG_LEVEL="INFO"

4. Testing with MCP Inspector

Use the official MCP Inspector to test and interact with your server:

1. Start the server:

   uv run python -m airtable_mcp http
   

2. Open MCP Inspector: Visit https://modelcontextprotocol.io/docs/tools/inspector.
3. Connect to your server:
   • Select "HTTP Streaming" transport.
   • Enter the URL: http://localhost:8000/mcp.
   • Click "Connect".
4. Authenticate with Airtable:
   • The server will guide you through OAuth authentication.
   • Use the inspector to test available MCP tools.

5. Run the Server

STDIO Transport (default):

uv run python -m airtable_mcp
# or
uv run airtable-oauth-mcp

HTTP Transport:

uv run python -m airtable_mcp http
# or with custom host/port
uv run python -m airtable_mcp http localhost 8001

Additional Options:

# Set log level
uv run python -m airtable_mcp --log-level DEBUG

# Show help
uv run python -m airtable_mcp --help

# Show version
uv run python -m airtable_mcp --version

The HTTP server will be available at http://localhost:8000/ (or custom host:port) with OAuth endpoints for web integration.

✨ Features

Core Functionality

• 🔐 OAuth 2.0 Authentication: Secure token-based authentication with Airtable.
• 📊 Complete Airtable API Coverage: 10 comprehensive MCP tools covering all operations.
• ⚡ FastMCP Framework: Built on the high - performance FastMCP framework.
• ☁️ Cloud - Ready: Production - ready deployment support.
• 🔄 Dual Transport: Support for both STDIO and HTTP transport protocols.

Security & Reliability

• 🔑 Environment - based Configuration: Secure credential management.
• ✅ Type Safety: Full type hints and validation with Pydantic.
• 🧪 Comprehensive Testing: Unit tests with pytest and coverage reporting.
• 📝 Code Quality: Linting with Ruff and type checking with MyPy.

Developer Experience

• 📚 Rich Documentation: Comprehensive setup and usage guides.
• 🔧 Easy Setup: Simple installation with uv package manager.
• 🎯 Typed Parameters: Clear, typed tool parameters for better IDE support.
• 🔍 Flexible Querying: Advanced filtering, sorting, and search capabilities.

📦 Installation

Prerequisites

• Python 3.11+: Latest Python version for optimal performance.
• uv: Fast Python package manager ([install guide](https://docs.astral.sh/uv/getting - started/installation/)).
• Airtable Developer Account: To create OAuth applications (sign up).

💻 Usage Examples

Basic Usage

# List all records in a table
records = await client.call_tool("list_records", {
    "base_id": "appXXXXXXXXXXXXXX",
    "table_id": "tblYYYYYYYYYYYYYY"
})

# Create a new record
new_record = await client.call_tool("create_record", {
    "base_id": "appXXXXXXXXXXXXXX",
    "table_id": "tblYYYYYYYYYYYYYY",
    "fields": {
        "Name": "John Doe",
        "Email": "john@example.com",
        "Status": "Active"
    }
})

# Search records with filtering
filtered_records = await client.call_tool("search_records", {
    "base_id": "appXXXXXXXXXXXXXX",
    "table_id": "tblYYYYYYYYYYYYYY",
    "filter_by_formula": "AND({Status} = 'Active', {Email} != '')",
    "fields": ["Name", "Email", "Status"]
})

Advanced Usage

# List records with sorting and filtering
records = await client.call_tool("list_records", {
    "base_id": "appXXXXXXXXXXXXXX",
    "table_id": "tblYYYYYYYYYYYYYY",
    "view": "Grid view",
    "filter_by_formula": "{Priority} = 'High'",
    "sort": [
        {"field": "Created", "direction": "desc"},
        {"field": "Name", "direction": "asc"}
    ],
    "fields": ["Name", "Priority", "Created", "Status"]
})

# Batch operations
batch_create = await client.call_tool("create_records", {
    "base_id": "appXXXXXXXXXXXXXX",
    "table_id": "tblYYYYYYYYYYYYYY",
    "records": [
        {"fields": {"Name": "Record 1", "Value": 100}},
        {"fields": {"Name": "Record 2", "Value": 200}},
        {"fields": {"Name": "Record 3", "Value": 300}}
    ],
    "typecast": True
})

Schema Discovery

# List all bases you have access to
bases = await client.call_tool("list_bases")

# Get detailed information about a specific table
table_info = await client.call_tool("describe_table", {
    "base_id": "appXXXXXXXXXXXXXX",
    "table_id": "tblYYYYYYYYYYYYYY"
})

# List all tables in a base
tables = await client.call_tool("list_tables", {
    "base_id": "appXXXXXXXXXXXXXX",
    "detail_level": "full"
})

📚 Documentation

MCP Tools Available

The server provides 10 MCP tools for Airtable operations: Base Operations:

• list_bases(): List all accessible bases.
• list_tables(base_id, detail_level?): List tables in a base.
• describe_table(base_id, table_id): Get detailed table schema.

Record Operations:

• list_records(base_id, table_id, view?, filter_by_formula?, sort?, fields?): List records with filtering.
• get_record(base_id, table_id, record_id): Get a specific record.
• create_record(base_id, table_id, fields, typecast?): Create a single record.
• create_records(base_id, table_id, records, typecast?): Create multiple records.
• update_records(base_id, table_id, records, typecast?): Update multiple records.
• delete_records(base_id, table_id, record_ids): Delete multiple records.
• search_records(base_id, table_id, filter_by_formula, view?, fields?): Search records with formulas.

All tools now use typed parameters instead of generic args, making them more transparent to MCP clients.

Parameter Flexibility:

• fields parameter accepts either a single field name (string) or array of field names.
• sort parameter expects array of objects: [{"field": "Name", "direction": "asc"}].

Additional Resources

• OAuth2 flow documentation
• Dynamic client registration
• Airtable API Reference
• MCP Specification

🔧 Technical Details

Development

Getting Started

1. Fork and Clone:

   git clone https://github.com/onimsha/airtable-mcp-server-oauth.git
   cd airtable-mcp-server-oauth
   

2. Setup Development Environment:

   uv sync --all - extras
   

3. Run Tests:

   uv run pytest
   uv run pytest --cov=src/airtable_mcp --cov - report=html
   

Code Quality

Type Checking:

uv run mypy src/

Linting:

uv run ruff check src/
uv run ruff format src/

Pre - commit Hooks:

pip install pre - commit
pre - commit install

Testing

The project includes comprehensive test coverage:

• Unit Tests: Test individual components and functions.
• Integration Tests: Test OAuth flow and Airtable API interactions.
• Coverage Reports: Ensure >90% code coverage.

# Run all tests
uv run pytest

# Run with coverage
uv run pytest --cov=src/airtable_mcp

# Run specific test files
uv run pytest tests/test_oauth.py
uv run pytest tests/test_tools.py

Project Structure

src/
├── airtable_mcp/           # Main MCP server package
│   ├── __init__.py         # Package initialization
│   ├── __main__.py         # Module entry point
│   ├── main.py             # CLI and application entry
│   ├── api/                # Airtable API client
│   │   ├── __init__.py
│   │   ├── client.py       # HTTP client for Airtable API
│   │   ├── exceptions.py   # API - specific exceptions
│   │   └── models.py       # Pydantic models for API responses
│   └── mcp/                # MCP server implementation
│       ├── __init__.py
│       ├── schemas.py      # MCP tool schemas
│       └── server.py       # FastMCP server with tools
└── mcp_oauth_lib/          # Reusable OAuth library
    ├── __init__.py         # Library initialization
    ├── auth/               # Authentication components
    │   ├── __init__.py
    │   ├── context.py      # Auth context management
    │   ├── middleware.py   # OAuth middleware
    │   └── utils.py        # Auth utilities
    ├── core/               # Core OAuth functionality
    │   ├── __init__.py
    │   ├── config.py       # OAuth configuration
    │   ├── flow.py         # OAuth flow implementation
    │   └── server.py       # OAuth server endpoints
    ├── providers/          # OAuth provider implementations
    │   ├── __init__.py
    │   ├── airtable.py     # Airtable OAuth provider
    │   └── base.py         # Base provider interface
    └── utils/              # OAuth utilities
        ├── __init__.py
        ├── pkce.py         # PKCE implementation
        └── state.py        # State management

Configuration

All configuration is handled through environment variables (loaded from .env):

🤝 Contributing

We welcome contributions! Please see our contribution guidelines:

1. Fork the repository and create a feature branch.
2. Write tests for any new functionality.
3. Ensure code quality with our linting and formatting tools.
4. Update documentation for any API changes.
5. Submit a pull request with a clear description.

Contribution Areas

• 🐛 Bug fixes: Help us squash bugs.
• ✨ New features: Add new Airtable API endpoints.
• 📚 Documentation: Improve setup guides and examples.
• 🧪 Testing: Increase test coverage.
• 🚀 Performance: Optimize API calls and caching.

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

🙏 Acknowledgments

• FastMCP: Excellent MCP framework.
• Airtable: Powerful database platform.
• Model Context Protocol: Standard for AI tool integration.

📞 Support

• Issues: GitHub Issues
• Discussions: GitHub Discussions
• Documentation: Project Wiki