Connectto Perplexity, Klavis ReportGen, Motion MCP Servers

Create powerful collaborative AI workflows by connecting multiple MCP servers including Perplexity, Klavis ReportGen, Motion for enhanced multi-agent automation capabilities in Klavis AI.

Perplexity icon

Perplexity

coming soon

Perplexity is an AI research assistant that provides accurate answers and cites sources

Klavis ReportGen icon

Klavis ReportGen

featured

Generate visually appealing JavaScript web reports from search queries with Klavis AI.

Available Tools:

  • generate_web_reports
Motion icon

Motion

featured

Motion is an intelligent project management and calendar application that automatically schedules your tasks, meetings, and projects to optimize your productivity and help you focus on what matters most

Available Tools:

  • motion_get_workspaces
  • motion_get_users
  • motion_get_my_user
  • +11 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"))

perplexity_mcp_instance = klavis_client.mcp_server.create_server_instance(
    server_name=McpServerName.PERPLEXITY,
    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,
)

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

# Initialize MCP tools for each server
perplexity_tools = MCPServerAdapter(perplexity_mcp_instance.server_params)
klavis_reportgen_tools = MCPServerAdapter(klavis_reportgen_mcp_instance.server_params)
motion_tools = MCPServerAdapter(motion_mcp_instance.server_params)

# Create specialized agents for each service
perplexity_agent = Agent(
    role="Perplexity Specialist",
    goal="Handle all Perplexity related tasks and data processing",
    backstory="You are an expert in Perplexity operations and data analysis",
    tools=perplexity_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
)

motion_agent = Agent(
    role="Motion Specialist",
    goal="Handle all Motion related tasks and data processing",
    backstory="You are an expert in Motion operations and data analysis",
    tools=motion_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=perplexity_agent,
    markdown=True
)

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

# Create multi-agent crew
multi_agent_crew = Crew(
    agents=[perplexity_agent, klavis_reportgen_agent, motion_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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