Connectto Motion, Fathom MCP Servers

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

Motion icon

Motion

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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
Fathom icon

Fathom

featured

Fathom is an AI meeting assistant that records, transcribes, and summarizes your video calls. Automatically capture meeting notes, action items, and key insights from Zoom, Google Meet, and Microsoft Teams meetings

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"))

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

fathom_mcp_instance = klavis_client.mcp_server.create_server_instance(
    server_name=McpServerName.FATHOM,
    user_id="1234",
    connection_type=ConnectionType.STREAMABLE_HTTP,
)

# Initialize MCP tools for each server
motion_tools = MCPServerAdapter(motion_mcp_instance.server_params)
fathom_tools = MCPServerAdapter(fathom_mcp_instance.server_params)

# Create specialized agents for each service
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
)

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

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

# Create multi-agent crew
multi_agent_crew = Crew(
    agents=[motion_agent, fathom_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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