Connectto Attio, ClickUp, Doc2markdown MCP Servers

Create powerful collaborative AI workflows by connecting multiple MCP servers including Attio, ClickUp, Doc2markdown for enhanced multi-agent automation capabilities in Klavis AI.

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
ClickUp icon

ClickUp

featured

ClickUp is a comprehensive project management and productivity platform that helps teams organize tasks, manage projects, and collaborate effectively with customizable workflows and powerful tracking features

Available Tools:

  • clickup_get_teams
  • clickup_get_workspaces
  • clickup_get_spaces
  • +18 more tools
Doc2markdown icon

Doc2markdown

featured

Convert any file to markdown using markitdown

Available Tools:

  • convert_document_to_markdown

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"))

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,
)

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

doc2markdown_mcp_instance = klavis_client.mcp_server.create_server_instance(
    server_name=McpServerName.DOC2MARKDOWN,
    user_id="1234",
    platform_name="Klavis",
    connection_type=ConnectionType.STREAMABLE_HTTP,
)

# Initialize MCP tools for each server
attio_tools = MCPServerAdapter(attio_mcp_instance.server_params)
clickup_tools = MCPServerAdapter(clickup_mcp_instance.server_params)
doc2markdown_tools = MCPServerAdapter(doc2markdown_mcp_instance.server_params)

# Create specialized agents for each service
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
)

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

doc2markdown_agent = Agent(
    role="Doc2markdown Specialist",
    goal="Handle all Doc2markdown related tasks and data processing",
    backstory="You are an expert in Doc2markdown operations and data analysis",
    tools=doc2markdown_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=attio_agent,
    markdown=True
)

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

# Create multi-agent crew
multi_agent_crew = Crew(
    agents=[attio_agent, clickup_agent, doc2markdown_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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