Airtable MCP Agent - MVP

AI agent that generates and executes Python code to interact with Airtable using the MCP code execution pattern from Anthropic's blog.

Architecture

User Input → Claude Sonnet 4 → Python Code → Subprocess → Airtable MCP → Results

Key benefit: Process data in code, not in context.

Quick Start

1. Prerequisites

# Airtable MCP server running at http://localhost:8000/mcp
# Anthropic API key

2. Install

pip install langchain langchain-anthropic anthropic aiohttp

3. Set API Key

export ANTHROPIC_API_KEY="your-anthropic-api-key"

4. Run

python cli.py

Usage

You: List all my Airtable bases

🤖 Agent: Thinking...

Generated Code (Attempt 1):
============================================================
from airtable_client import airtable_client

async with airtable_client("http://localhost:8000/mcp") as client:
    from servers import airtable
    
    bases = await airtable.list_bases()
    print(json.dumps({"bases": bases}))
============================================================

✅ Success!

Output:
------------------------------------------------------------
{"bases": [{"id": "appXXX", "name": "My Base"}]}
------------------------------------------------------------

Example Queries

  "List all my Airtable bases"
  "Show me tables in base appXXXXXXXXXXXXXX"
  "Get all active Orders from my CRM base"
  "Find records containing 'john@example.com'"
  "Count records in each table of my base"

Commands

/help   - Show help
/clear  - Clear conversation history
/exit   - Exit

How It Works

1. User request → "Get all active contacts"
2. Claude generates code:

   records = await airtable.list_records(...)
   active = [r for r in records if r['fields']['Status'] == 'Active']
   print(len(active))  # Only summary to context!

3. Execute in subprocess (sandboxed, validated)
4. Return results to user

Full Writeup

https://medium.com/@shamsul.arefin/building-an-ai-agent-with-mcp-code-execution-from-confusion-to-clarity-6b13fccc8c4b