Connectto Asana, Calendly, Figma MCP Servers

Create powerful collaborative AI workflows by connecting multiple MCP servers including Asana, Calendly, Figma for enhanced multi-agent automation capabilities in Klavis AI.

Asana icon

Asana

featured

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
Calendly icon

Calendly

coming soon

Manage scheduling and appointments with your agents.

Figma icon

Figma

featured

Figma is a collaborative interface design tool for web and mobile applications.

Quick Setup Guide

Follow these steps to connect CrewAI to these MCP servers

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 your desired MCP servers 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,
)

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

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

# Initialize MCP tools for each server
asana_tools = MCPServerAdapter(asana_mcp_instance.server_params)
calendly_tools = MCPServerAdapter(calendly_mcp_instance.server_params)
figma_tools = MCPServerAdapter(figma_mcp_instance.server_params)

# Create specialized agents for each service
asana_agent = Agent(
    role="Asana Specialist",
    goal="Handle all Asana related tasks and data processing",
    backstory="You are an expert in Asana operations and data analysis",
    tools=asana_tools,
    reasoning=True,
    verbose=False
)

calendly_agent = Agent(
    role="Calendly Specialist",
    goal="Handle all Calendly related tasks and data processing",
    backstory="You are an expert in Calendly operations and data analysis",
    tools=calendly_tools,
    reasoning=True,
    verbose=False
)

figma_agent = Agent(
    role="Figma Specialist",
    goal="Handle all Figma related tasks and data processing",
    backstory="You are an expert in Figma operations and data analysis",
    tools=figma_tools,
    reasoning=True,
    verbose=False
)

# Define collaborative tasks
research_task = Task(
    description="Gather comprehensive data from all available sources",
    expected_output="Raw data and initial findings from all services",
    agent=asana_agent,
    markdown=True
)

analysis_task = Task(
    description="Analyze and synthesize the gathered data",
    expected_output="Comprehensive analysis with insights and recommendations",
    agent=calendly_agent,
    markdown=True
)

# Create multi-agent crew
multi_agent_crew = Crew(
    agents=[asana_agent, calendly_agent, figma_agent],
    tasks=[research_task, analysis_task],
    verbose=False,
    process=Process.sequential
)

result = multi_agent_crew.kickoff()

Frequently Asked Questions

Everything you need to know about connecting CrewAI to these MCP servers

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