Connectto Asana, Markdown2doc, Attio MCP Servers

Create powerful collaborative AI workflows by connecting multiple MCP servers including Asana, Markdown2doc, Attio 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
Markdown2doc icon

Markdown2doc

featured

Convert markdown text to different file formats (pdf, docx, doc, html), based on Pandoc

Available Tools:

  • convert_markdown_to_file
Attio icon

Attio

featured

Attio is a next-generation CRM platform that helps teams build stronger relationships with their customers through powerful data management and automation

Available Tools:

  • attio_search_people
  • attio_search_companies
  • attio_search_deals
  • +2 more tools

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,
)

markdown2doc_mcp_instance = klavis_client.mcp_server.create_server_instance(
    server_name=McpServerName.MARKDOWN2DOC,
    user_id="1234",
    platform_name="Klavis",
    connection_type=ConnectionType.STREAMABLE_HTTP,
)

attio_mcp_instance = klavis_client.mcp_server.create_server_instance(
    server_name=McpServerName.ATTIO,
    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)
markdown2doc_tools = MCPServerAdapter(markdown2doc_mcp_instance.server_params)
attio_tools = MCPServerAdapter(attio_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
)

markdown2doc_agent = Agent(
    role="Markdown2doc Specialist",
    goal="Handle all Markdown2doc related tasks and data processing",
    backstory="You are an expert in Markdown2doc operations and data analysis",
    tools=markdown2doc_tools,
    reasoning=True,
    verbose=False
)

attio_agent = Agent(
    role="Attio Specialist",
    goal="Handle all Attio related tasks and data processing",
    backstory="You are an expert in Attio operations and data analysis",
    tools=attio_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=markdown2doc_agent,
    markdown=True
)

# Create multi-agent crew
multi_agent_crew = Crew(
    agents=[asana_agent, markdown2doc_agent, attio_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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