Connectto Asana MCP Server

Seamlessly integrate your CrewAI multi-agent systems with Asana using Klavis AI's comprehensive MCP server connection guide.

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Asana

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Asana is a web and mobile application designed to help teams organize, track, and manage their work. It provides project management tools, task assignment, collaboration features, and progress tracking to boost team productivity

Available Tools:

  • asana_create_task
  • asana_get_task
  • asana_search_tasks
  • +16 more tools

Quick Setup Guide

Follow these steps to connect CrewAI to this MCP server

1

Create Your Account

Sign up for KlavisAI to access our MCP server management platform.

2

Configure Agents & Tools

Set up your CrewAI agents with the MCP server tools and configure authentication settings for collaborative workflows.

3

Deploy Your Crew

Test your multi-agent workflows and start using your enhanced collaborative AI capabilities.

CrewAI + KlavisAI Integration Snippets

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"))

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

with MCPServerAdapter(asana_mcp_instance.server_params) as mcp_tools:
    # Create a Asana Analysis Agent
    asana_agent = Agent(
        role="Asana Analyst",
        goal="Research and analyze asana to extract comprehensive insights",
        backstory="You are an expert at analyzing asana and creating professional summaries.",
        tools=mcp_tools,
        reasoning=True,
        verbose=False
    )
    
    # Define Task
    analysis_task = Task(
        description=f"Research and analyze asana 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=asana_agent,
        markdown=True
    )
    
    # Create and execute the crew
    asana_crew = Crew(
        agents=[asana_agent],
        tasks=[analysis_task],
        verbose=False,
        process=Process.sequential
    )
    
    result = asana_crew.kickoff()

Frequently Asked Questions

Everything you need to know about connecting CrewAI to this MCP server

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