Connectto Stripe, Slack, Motion MCP Servers
Create powerful collaborative AI workflows by connecting multiple MCP servers including Stripe, Slack, Motion for enhanced multi-agent automation capabilities in Klavis AI.
Stripe
Stripe is a suite of payment APIs that powers commerce for online businesses
Slack
Slack is a messaging app for business that connects people to the information they need
Available Tools:
- slack_list_channels
- slack_post_message
- slack_reply_to_thread
- +6 more tools
Motion
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
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"))
stripe_mcp_instance = klavis_client.mcp_server.create_server_instance(
server_name=McpServerName.STRIPE,
user_id="1234",
platform_name="Klavis",
connection_type=ConnectionType.STREAMABLE_HTTP,
)
slack_mcp_instance = klavis_client.mcp_server.create_server_instance(
server_name=McpServerName.SLACK,
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
stripe_tools = MCPServerAdapter(stripe_mcp_instance.server_params)
slack_tools = MCPServerAdapter(slack_mcp_instance.server_params)
motion_tools = MCPServerAdapter(motion_mcp_instance.server_params)
# Create specialized agents for each service
stripe_agent = Agent(
role="Stripe Specialist",
goal="Handle all Stripe related tasks and data processing",
backstory="You are an expert in Stripe operations and data analysis",
tools=stripe_tools,
reasoning=True,
verbose=False
)
slack_agent = Agent(
role="Slack Specialist",
goal="Handle all Slack related tasks and data processing",
backstory="You are an expert in Slack operations and data analysis",
tools=slack_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=stripe_agent,
markdown=True
)
analysis_task = Task(
description="Analyze and synthesize the gathered data",
expected_output="Comprehensive analysis with insights and recommendations",
agent=slack_agent,
markdown=True
)
# Create multi-agent crew
multi_agent_crew = Crew(
agents=[stripe_agent, slack_agent, motion_agent],
tasks=[research_task, analysis_task],
verbose=False,
process=Process.sequential
)
result = multi_agent_crew.kickoff()Frequently Asked Questions
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