Connectto Klavis ReportGen, GitHub, WhatsApp MCP Servers

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

Klavis ReportGen icon

Klavis ReportGen

featured

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

Available Tools:

  • generate_web_reports
GitHub icon

GitHub

featured

Enhanced GitHub MCP Server

Available Tools:

  • github_get_me
  • github_get_issue
  • github_search_issues
  • +31 more tools
WhatsApp icon

WhatsApp

featured

WhatsApp Business API integration that enables sending text messages, media, and managing conversations with customers. Perfect for customer support, marketing campaigns, and automated messaging workflows through the official WhatsApp Business platform.

Available Tools:

  • whatsapp_send_text

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"))

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,
)

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

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

# Initialize MCP tools for each server
klavis_reportgen_tools = MCPServerAdapter(klavis_reportgen_mcp_instance.server_params)
github_tools = MCPServerAdapter(github_mcp_instance.server_params)
whatsapp_tools = MCPServerAdapter(whatsapp_mcp_instance.server_params)

# Create specialized agents for each service
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
)

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

whatsapp_agent = Agent(
    role="WhatsApp Specialist",
    goal="Handle all WhatsApp related tasks and data processing",
    backstory="You are an expert in WhatsApp operations and data analysis",
    tools=whatsapp_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=klavis_reportgen_agent,
    markdown=True
)

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

# Create multi-agent crew
multi_agent_crew = Crew(
    agents=[klavis_reportgen_agent, github_agent, whatsapp_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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