Connectto Gong, Salesforce, Calendly MCP Servers

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

Gong icon

Gong

featured

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
Salesforce icon

Salesforce

featured

Salesforce is the world's leading customer relationship management (CRM) platform that helps businesses connect with customers, partners, and potential customers

Available Tools:

  • salesforce_get_accounts
  • salesforce_create_account
  • salesforce_update_account
  • +24 more tools
Calendly icon

Calendly

coming soon

Manage scheduling and appointments with your agents.

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"))

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,
)

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

calendly_mcp_instance = klavis_client.mcp_server.create_server_instance(
    server_name=McpServerName.CALENDLY,
    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)
salesforce_tools = MCPServerAdapter(salesforce_mcp_instance.server_params)
calendly_tools = MCPServerAdapter(calendly_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
)

salesforce_agent = Agent(
    role="Salesforce Specialist",
    goal="Handle all Salesforce related tasks and data processing",
    backstory="You are an expert in Salesforce operations and data analysis",
    tools=salesforce_tools,
    reasoning=True,
    verbose=False
)

calendly_agent = Agent(
    role="Calendly Specialist",
    goal="Handle all Calendly related tasks and data processing",
    backstory="You are an expert in Calendly operations and data analysis",
    tools=calendly_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=salesforce_agent,
    markdown=True
)

# Create multi-agent crew
multi_agent_crew = Crew(
    agents=[gong_agent, salesforce_agent, calendly_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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