Connectto Gong, Cloudflare, Slack MCP Servers
Create powerful collaborative AI workflows by connecting multiple MCP servers including Gong, Cloudflare, Slack for enhanced multi-agent automation capabilities in Klavis AI.
Gong
Gong is a revenue intelligence platform that captures and analyzes all revenue-related interactions to help sales teams close more deals. It provides conversation analytics, deal insights, and sales performance tracking through call recordings and transcripts
Available Tools:
- gong_get_transcripts_by_user
- gong_get_extensive_data
- gong_get_call_transcripts
- +2 more tools
Cloudflare
Cloudflare provides content delivery network services, DDoS protection, and security.
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
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"))
gong_mcp_instance = klavis_client.mcp_server.create_server_instance(
server_name=McpServerName.GONG,
user_id="1234",
platform_name="Klavis",
connection_type=ConnectionType.STREAMABLE_HTTP,
)
cloudflare_mcp_instance = klavis_client.mcp_server.create_server_instance(
server_name=McpServerName.CLOUDFLARE,
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,
)
# Initialize MCP tools for each server
gong_tools = MCPServerAdapter(gong_mcp_instance.server_params)
cloudflare_tools = MCPServerAdapter(cloudflare_mcp_instance.server_params)
slack_tools = MCPServerAdapter(slack_mcp_instance.server_params)
# Create specialized agents for each service
gong_agent = Agent(
role="Gong Specialist",
goal="Handle all Gong related tasks and data processing",
backstory="You are an expert in Gong operations and data analysis",
tools=gong_tools,
reasoning=True,
verbose=False
)
cloudflare_agent = Agent(
role="Cloudflare Specialist",
goal="Handle all Cloudflare related tasks and data processing",
backstory="You are an expert in Cloudflare operations and data analysis",
tools=cloudflare_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
)
# 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=gong_agent,
markdown=True
)
analysis_task = Task(
description="Analyze and synthesize the gathered data",
expected_output="Comprehensive analysis with insights and recommendations",
agent=cloudflare_agent,
markdown=True
)
# Create multi-agent crew
multi_agent_crew = Crew(
agents=[gong_agent, cloudflare_agent, slack_agent],
tasks=[research_task, analysis_task],
verbose=False,
process=Process.sequential
)
result = multi_agent_crew.kickoff()Frequently Asked Questions
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