Connectto Slack, Exa, Google Jobs MCP Servers

Create powerful collaborative AI workflows by connecting multiple MCP servers including Slack, Exa, Google Jobs for enhanced multi-agent automation capabilities in Klavis AI.

Slack icon

Slack

featured

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
Exa icon

Exa

featured

Exa is an AI-powered search engine designed for AI applications. Use neural search to understand meaning and context, find similar content, get direct answers with citations, and conduct comprehensive research with structured analysis

Available Tools:

  • exa_search
  • exa_get_contents
  • exa_find_similar
  • +2 more tools
Google Jobs icon

Google Jobs

featured

Google Jobs is a comprehensive job search platform that aggregates listings from across the web. Search for jobs by location, company, employment type, and more, with detailed information about requirements, benefits, and application processes

Available Tools:

  • google_jobs_search
  • google_jobs_get_details
  • google_jobs_search_by_company
  • +2 more tools

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"))

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,
)

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

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

# Initialize MCP tools for each server
slack_tools = MCPServerAdapter(slack_mcp_instance.server_params)
exa_tools = MCPServerAdapter(exa_mcp_instance.server_params)
google_jobs_tools = MCPServerAdapter(google_jobs_mcp_instance.server_params)

# Create specialized agents for each service
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
)

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

google_jobs_agent = Agent(
    role="Google Jobs Specialist",
    goal="Handle all Google Jobs related tasks and data processing",
    backstory="You are an expert in Google Jobs operations and data analysis",
    tools=google_jobs_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=slack_agent,
    markdown=True
)

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

# Create multi-agent crew
multi_agent_crew = Crew(
    agents=[slack_agent, exa_agent, google_jobs_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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