Connectto Linear, Zendesk, GitHub MCP Servers

Create powerful collaborative AI workflows by connecting multiple MCP servers including Linear, Zendesk, GitHub for enhanced multi-agent automation capabilities in Klavis AI.

Linear icon

Linear

featured

Linear is a modern issue tracking and project management tool designed for high-performance teams to build better software faster

Available Tools:

  • linear_get_teams
  • linear_get_issues
  • linear_get_issue_by_id
  • +9 more tools
Zendesk icon

Zendesk

coming soon

Zendesk is a customer service software company

GitHub icon

GitHub

featured

Enhanced GitHub MCP Server

Available Tools:

  • github_get_me
  • github_get_issue
  • github_search_issues
  • +31 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"))

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

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

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

# Initialize MCP tools for each server
linear_tools = MCPServerAdapter(linear_mcp_instance.server_params)
zendesk_tools = MCPServerAdapter(zendesk_mcp_instance.server_params)
github_tools = MCPServerAdapter(github_mcp_instance.server_params)

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

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

github_agent = Agent(
    role="GitHub Specialist",
    goal="Handle all GitHub related tasks and data processing",
    backstory="You are an expert in GitHub operations and data analysis",
    tools=github_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=linear_agent,
    markdown=True
)

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

# Create multi-agent crew
multi_agent_crew = Crew(
    agents=[linear_agent, zendesk_agent, github_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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