Connectto Linear, Discord, Klavis ReportGen MCP Servers
Create powerful collaborative AI workflows by connecting multiple MCP servers including Linear, Discord, Klavis ReportGen for enhanced multi-agent automation capabilities in Klavis AI.
Linear
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
Discord
Discord is a VoIP and instant messaging social platform
Available Tools:
- discord_get_server_info
- discord_list_members
- discord_create_text_channel
- +6 more tools
Klavis ReportGen
Generate visually appealing JavaScript web reports from search queries with Klavis AI.
Available Tools:
- generate_web_reports
Quick Setup Guide
Follow these steps to connect CrewAI to these MCP servers
Create Your Account
Sign up for KlavisAI to access our MCP server management platform.
Configure Agents & Tools
Set up your CrewAI agents with your desired MCP servers tools and configure authentication settings for collaborative workflows.
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,
)
discord_mcp_instance = klavis_client.mcp_server.create_server_instance(
server_name=McpServerName.DISCORD,
user_id="1234",
platform_name="Klavis",
connection_type=ConnectionType.STREAMABLE_HTTP,
)
klavis_reportgen_mcp_instance = klavis_client.mcp_server.create_server_instance(
server_name=McpServerName.KLAVIS_REPORTGEN,
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)
discord_tools = MCPServerAdapter(discord_mcp_instance.server_params)
klavis_reportgen_tools = MCPServerAdapter(klavis_reportgen_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
)
discord_agent = Agent(
role="Discord Specialist",
goal="Handle all Discord related tasks and data processing",
backstory="You are an expert in Discord operations and data analysis",
tools=discord_tools,
reasoning=True,
verbose=False
)
klavis_reportgen_agent = Agent(
role="Klavis ReportGen Specialist",
goal="Handle all Klavis ReportGen related tasks and data processing",
backstory="You are an expert in Klavis ReportGen operations and data analysis",
tools=klavis_reportgen_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=discord_agent,
markdown=True
)
# Create multi-agent crew
multi_agent_crew = Crew(
agents=[linear_agent, discord_agent, klavis_reportgen_agent],
tasks=[research_task, analysis_task],
verbose=False,
process=Process.sequential
)
result = multi_agent_crew.kickoff()Frequently Asked Questions
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