databasesdeveloper-tools

Airtable OAuth MCP Server

onimsha

by onimsha

Production-ready MCP server for secure Airtable OAuth, enabling AI assistants and apps to interact with Airtable bases v

A production-ready Model Context Protocol server that enables AI assistants and applications to interact with Airtable bases through a standardized interface with secure OAuth 2.0 authentication.

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Both formats append explainx.ai attribution and the canonical URL for this MCP server listing.

Production-ready with OAuth securityComplete Airtable API coverageBuilt on FastMCP framework

best for

  • / AI assistants managing Airtable data
  • / Automating Airtable workflows
  • / Building apps that integrate with Airtable
  • / Enterprise teams requiring secure database access

capabilities

  • / Create and update Airtable records
  • / Query tables with filtering and sorting
  • / Manage table schemas and fields
  • / Search across multiple bases
  • / Delete records and tables
  • / Authenticate via OAuth 2.0

what it does

Connects AI assistants to Airtable bases through secure OAuth authentication. Provides complete access to all Airtable operations via the Model Context Protocol interface.

about

Airtable OAuth MCP Server is a community-built MCP server published by onimsha that provides AI assistants with tools and capabilities via the Model Context Protocol. Production-ready MCP server for secure Airtable OAuth, enabling AI assistants and apps to interact with Airtable bases v It is categorized under databases, developer tools.

how to install

You can install Airtable OAuth MCP Server in your AI client of choice. Use the install panel on this page to get one-click setup for Cursor, Claude Desktop, VS Code, and other MCP-compatible clients. This server runs locally on your machine via the stdio transport.

license

MIT

Airtable OAuth MCP Server is released under the MIT license. This is a permissive open-source license, meaning you can freely use, modify, and distribute the software.

readme

Airtable OAuth MCP Server

Python License Code style: ruff

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.

🚀 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

📋 Prerequisites

  • Python 3.11+ - Latest Python version for optimal performance
  • uv - Fast Python package manager (install guide)
  • Airtable Developer Account - To create OAuth applications (sign up)

🚀 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.

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"}]

💡 Usage Examples

Basic Record Operations

# 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": "[email protected]",
        "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 Querying

# 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"
})

🛠️ 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/              

---

FAQ

What is the Airtable OAuth MCP Server MCP server?
Airtable OAuth MCP Server is a Model Context Protocol (MCP) server profile on explainx.ai. MCP lets AI hosts (e.g. Claude Desktop, Cursor) call tools and resources through a standard interface; this page summarizes categories, install hints, and community ratings.
How do MCP servers relate to agent skills?
Skills are reusable instruction packages (often SKILL.md); MCP servers expose live capabilities. Teams frequently combine both—skills for workflows, MCP for APIs and data. See explainx.ai/skills and explainx.ai/mcp-servers for parallel directories.
How are reviews shown for Airtable OAuth MCP Server?
This profile displays 47 aggregated ratings (sample rows for discoverability plus signed-in user reviews). Average score is about 4.5 out of 5—verify behavior in your own environment before production use.

Use Cases

Direct Database Queries from AI

Enable Claude to query your database directly using natural language

Example

Ask 'Show me top 10 customers by revenue this month' and get SQL results instantly

Eliminate manual SQL writing for ad-hoc queries, get insights 10x faster

Data Analysis & Reporting

Generate complex reports and analytics without leaving conversation

Example

Analyze sales trends, cohort retention, user behavior patterns conversationally

Democratize data access—non-technical team members can query databases

Schema Exploration

Understand database structure, relationships, and data models

Example

'Explain the user_orders table schema and its relationships'

Onboard engineers faster, explore unfamiliar databases efficiently

Data Validation & Quality Checks

Run data quality queries to catch anomalies and inconsistencies

Example

Find duplicate records, missing values, orphaned foreign keys automatically

Maintain data integrity with less manual SQL work

Implementation Guide

Prerequisites

  • Claude Desktop 0.7.0+ or Cursor with MCP support
  • Database credentials (read-only recommended for safety)
  • Network access from Claude client to database
  • Understanding of database security and access control

Time Estimate

15-30 minutes including configuration and testing

Installation Steps

  1. 1.Install MCP server: npm install -g @modelcontextprotocol/server-[name]
  2. 2.Configure database connection in Claude Desktop config (~/.claude/mcp.json)
  3. 3.Provide connection string: host, port, database, username, password
  4. 4.Restart Claude Desktop to load MCP server
  5. 5.Test connection: 'List all tables in database'
  6. 6.Run simple query: 'Show me 5 rows from users table'
  7. 7.Verify results and permissions are correct
  8. 8.Document query patterns for team use

Troubleshooting

  • Connection refused: Check database is running and network accessible
  • Authentication failed: Verify credentials, check user permissions
  • Claude can't see tables: Grant appropriate read permissions to database user
  • Slow queries: Add indexes, limit result set size, use read replicas
  • MCP server not loading: Check config syntax, restart Claude Desktop

Best Practices

✓ Do

  • +Use read-only database credentials to prevent accidental writes
  • +Connect to read replica, not production primary database
  • +Set query timeout limits to prevent long-running queries
  • +Document database schema and common queries for AI context
  • +Monitor query performance and optimize slow queries
  • +Use connection pooling for better performance
  • +Test with non-production data first

✗ Don't

  • Don't use production write credentials—risk of data corruption
  • Don't query production database during peak traffic hours
  • Don't expose sensitive PII without proper access controls
  • Don't skip query result validation—AI can misinterpret schema
  • Don't allow unlimited result set sizes—set LIMIT clauses
  • Don't share database credentials in plain text config files

💡 Pro Tips

  • Create database views for common queries to simplify AI access
  • Add schema comments/descriptions so AI understands column meanings
  • Use semantic table/column names ('customer_lifetime_value' not 'clv')
  • Set up query logging to audit what Claude is querying
  • Create saved query templates for recurring analysis
  • Combine with data visualization tools for better insights

Technical Details

Architecture

MCP server acts as bridge between Claude and database, translating natural language to SQL queries and returning results in structured format.

Protocols

  • Model Context Protocol (MCP)
  • Database-specific protocols (PostgreSQL, MySQL, MongoDB)

Compatibility

  • PostgreSQL
  • MySQL
  • SQLite
  • MongoDB
  • Redis

When to Use This

✓ Use When

Use for ad-hoc data queries, exploratory analysis, report generation, schema exploration, and democratizing data access. Best for read-heavy analytics workloads.

✗ Avoid When

Avoid for production write operations, mission-critical transactions, real-time OLTP workloads, or when database contains sensitive PII without proper access controls. Use read replicas, not primary.

Integration

  • Read replica connection for analytics queries
  • Database view layer to abstract complex joins
  • Query result caching for repeated questions
  • Audit logging of all AI-generated queries

Discussion

Product Hunt–style comments (not star reviews)
  • No comments yet — start the thread.

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Ratings

4.547 reviews
  • Anika Desai· Dec 28, 2024

    We evaluated Airtable OAuth MCP Server against two servers with overlapping tools; this profile had the clearer scope statement.

  • Hassan Menon· Dec 24, 2024

    Useful MCP listing: Airtable OAuth MCP Server is the kind of server we cite when onboarding engineers to host + tool permissions.

  • Amelia Dixit· Dec 16, 2024

    I recommend Airtable OAuth MCP Server for teams standardizing on MCP; the explainx.ai page compares cleanly with sibling servers.

  • Rahul Santra· Nov 23, 2024

    Airtable OAuth MCP Server is a well-scoped MCP server in the explainx.ai directory — install snippets and categories matched our Claude Code setup.

  • Arjun Nasser· Nov 19, 2024

    Airtable OAuth MCP Server is among the better-indexed MCP projects we tried; the explainx.ai summary tracks the official description.

  • Anika Sanchez· Nov 15, 2024

    Airtable OAuth MCP Server reduced integration guesswork — categories and install configs on the listing matched the upstream repo.

  • Meera Chawla· Nov 7, 2024

    According to our notes, Airtable OAuth MCP Server benefits from clear Model Context Protocol framing — fewer ambiguous “AI plugin” claims.

  • Maya Ramirez· Oct 26, 2024

    Airtable OAuth MCP Server is among the better-indexed MCP projects we tried; the explainx.ai summary tracks the official description.

  • Pratham Ware· Oct 14, 2024

    Airtable OAuth MCP Server has been reliable for tool-calling workflows; the MCP profile page is a good permalink for internal docs.

  • Mei Martin· Oct 10, 2024

    According to our notes, Airtable OAuth MCP Server benefits from clear Model Context Protocol framing — fewer ambiguous “AI plugin” claims.

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