Airtable MCP Server Integration

What does “Airtable” MCP Server do?

The Airtable MCP (Model Context Protocol) Server is a specialized tool that connects AI assistants—such as Claude Desktop and other MCP-enabled clients—to Airtable’s API. This server enables programmatic management of Airtable bases, tables, fields, and records, automating workflows such as searching, creating, and updating data. By exposing Airtable functionality as MCP tools, it empowers developers and AI agents to perform database operations, structure or modify tables, and interact with content in a more agentic, reliable manner. Its system prompts and project knowledge resources further enhance the LLM’s effectiveness when working with Airtable data, streamlining integration and minimizing errors, especially during complex table-building scenarios.

List of Prompts

• system-prompt: Provides a foundational system prompt to guide LLM behavior when interacting with Airtable via the MCP server.
• project-knowledge: Supplies project-specific instructions and knowledge to help the LLM leverage Airtable projects effectively within clients like Claude Desktop.

List of Resources

• Bases Resource: Exposes metadata and access to all accessible Airtable bases for use as LLM context.
• Tables Resource: Makes available schema and structure for tables within a base, supporting intelligent table management.
• Fields Resource: Provides details about fields (columns) in a table, enabling informed field creation or editing.
• Records Resource: Surfaces data records from tables, facilitating data retrieval or manipulation via the LLM.

List of Tools

• list_bases: Lists all accessible Airtable bases for the connected user.
• list_tables: Lists all tables within a specified base.
• create_table: Creates a new table in a specified base, supporting field definitions.
• update_table: Updates the name or description of an existing table.
• create_field: Adds a new field (column) to an existing table.
• update_field: Modifies an existing field’s configuration.
• list_records: Retrieves records from a specified table.

Use Cases of this MCP Server

• Database Management: Allows developers to programmatically create, update, or structure Airtable bases and tables via AI assistants, streamlining database administration.
• Automated Data Entry: Enables AI-powered workflows to add or update records in tables, facilitating rapid data entry or cleansing.
• Schema Design and Exploration: Provides tools and resources for exploring table structures or designing new schemas directly from the LLM interface.
• Collaborative Project Tracking: Lets teams surface project-related Airtable data in natural language, improving project visibility and collaboration.
• Data Retrieval for Analysis: Supports extraction of records for reporting or analytics, making Airtable data more accessible for downstream tasks.

How to set it up

Windsurf

No explicit instructions found in the repository for Windsurf.

Claude

1. Ensure Node.js (v18+) and npm are installed.
2. Obtain your Airtable API key as described in the repo.
3. Navigate to the Claude configuration directory:
   • Windows: C:\Users\NAME\AppData\Roaming\Claude
   • macOS: ~/Library/Application Support/Claude/
4. Edit or create claude_desktop_config.json:

{
  "mcpServers": {
    "airtable": {
      "command": "npx",
      "args": ["@felores/airtable-mcp-server"],
      "env": {
        "AIRTABLE_API_KEY": "your_api_key_here"
      }
    }
  }
}

5. Save the config and restart Claude Desktop. The Airtable MCP server should appear under “Connected MCP Servers”.

Cursor

No explicit instructions found in the repository for Cursor.

Cline

No explicit instructions found in the repository for Cline.

Securing API Keys

The Airtable API key is set via environment variables in the env field of the MCP server configuration. Example (for Claude):

{
  "mcpServers": {
    "airtable": {
      "command": "npx",
      "args": ["@felores/airtable-mcp-server"],
      "env": {
        "AIRTABLE_API_KEY": "your_api_key_here"
      }
    }
  }
}

Note: Always keep your API key secure and do not hardcode it into shared files.

How to use this MCP inside flows

Using MCP in FlowHunt

To integrate MCP servers into your FlowHunt workflow, start by adding the MCP component to your flow and connecting it to your AI agent:

Click on the MCP component to open the configuration panel. In the system MCP configuration section, insert your MCP server details using this JSON format:

{
  "airtable": {
    "transport": "streamable_http",
    "url": "https://yourmcpserver.example/pathtothemcp/url"
  }
}

Once configured, the AI agent is now able to use this MCP as a tool with access to all its functions and capabilities. Remember to change “airtable” to whatever the actual name of your MCP server is and replace the URL with your own MCP server URL.

Overview

Our opinion

Based on the available documentation, Airtable MCP provides a focused, well-documented server with all core MCP features and a clear pathway for setup and use. However, some platform-specific setup instructions and advanced features like Roots and Sampling are either missing or not documented.

Score: 8/10
Airtable MCP delivers robust functionality and documentation for Claude and generic MCP setups, but lacks explicit details for all platforms and some advanced MCP features.

MCP Score