ConnectCrewAIto Airtable MCP Server

import os
from crewai import Agent, Task, Crew, Process
from crewai_tools import MCPServerAdapter
from klavis import Klavis
from klavis.types import McpServerName, ConnectionType

# Initialize clients
klavis_client = Klavis(api_key=os.getenv("KLAVIS_API_KEY"))

airtable_mcp_instance = klavis_client.mcp_server.create_server_instance(
    server_name=McpServerName.AIRTABLE,
    user_id="1234",
    platform_name="Klavis",
    connection_type=ConnectionType.STREAMABLE_HTTP,
)

with MCPServerAdapter(airtable_mcp_instance.server_params) as mcp_tools:
    # Create a Airtable Analysis Agent
    airtable_agent = Agent(
        role="Airtable Analyst",
        goal="Research and analyze airtable to extract comprehensive insights",
        backstory="You are an expert at analyzing airtable and creating professional summaries.",
        tools=mcp_tools,
        reasoning=True,
        verbose=False
    )
    
    # Define Task
    analysis_task = Task(
        description=f"Research and analyze airtable data. Extract relevant information and create a comprehensive summary with key points and main takeaways.",
        expected_output="Complete analysis with structured summary, key insights, and main takeaways",
        agent=airtable_agent,
        markdown=True
    )
    
    # Create and execute the crew
    airtable_crew = Crew(
        agents=[airtable_agent],
        tasks=[analysis_task],
        verbose=False,
        process=Process.sequential
    )
    
    result = airtable_crew.kickoff()