Connectto Postgres, LinkedIn, Discord MCP Servers

Create powerful collaborative AI workflows by connecting multiple MCP servers including Postgres, LinkedIn, Discord for enhanced multi-agent automation capabilities in Klavis AI.

Postgres icon

Postgres

featured

PostgreSQL is a powerful, open source object-relational database system

Available Tools:

  • query
LinkedIn icon

LinkedIn

coming soon

LinkedIn is a business and employment-oriented online service

Discord icon

Discord

featured

Discord is a VoIP and instant messaging social platform

Available Tools:

  • discord_get_server_info
  • discord_list_members
  • discord_create_text_channel
  • +6 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"))

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

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

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

# Initialize MCP tools for each server
postgres_tools = MCPServerAdapter(postgres_mcp_instance.server_params)
linkedin_tools = MCPServerAdapter(linkedin_mcp_instance.server_params)
discord_tools = MCPServerAdapter(discord_mcp_instance.server_params)

# Create specialized agents for each service
postgres_agent = Agent(
    role="Postgres Specialist",
    goal="Handle all Postgres related tasks and data processing",
    backstory="You are an expert in Postgres operations and data analysis",
    tools=postgres_tools,
    reasoning=True,
    verbose=False
)

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

discord_agent = Agent(
    role="Discord Specialist",
    goal="Handle all Discord related tasks and data processing",
    backstory="You are an expert in Discord operations and data analysis",
    tools=discord_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=postgres_agent,
    markdown=True
)

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

# Create multi-agent crew
multi_agent_crew = Crew(
    agents=[postgres_agent, linkedin_agent, discord_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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