Connectto Postgres, Firecrawl Deep Research, Resend MCP Servers

Create powerful collaborative AI workflows by connecting multiple MCP servers including Postgres, Firecrawl Deep Research, Resend 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
Firecrawl Deep Research icon

Firecrawl Deep Research

featured

A personal research assistant that analyze sources across the web, based on Firecrawl

Available Tools:

  • firecrawl_deep_research
Resend icon

Resend

featured

Resend is a modern email API for sending and receiving emails programmatically

Available Tools:

  • resend_send_email
  • resend_create_audience
  • resend_get_audience
  • +12 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,
)

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

resend_mcp_instance = klavis_client.mcp_server.create_server_instance(
    server_name=McpServerName.RESEND,
    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)
firecrawl_deep_research_tools = MCPServerAdapter(firecrawl_deep_research_mcp_instance.server_params)
resend_tools = MCPServerAdapter(resend_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
)

firecrawl_deep_research_agent = Agent(
    role="Firecrawl Deep Research Specialist",
    goal="Handle all Firecrawl Deep Research related tasks and data processing",
    backstory="You are an expert in Firecrawl Deep Research operations and data analysis",
    tools=firecrawl_deep_research_tools,
    reasoning=True,
    verbose=False
)

resend_agent = Agent(
    role="Resend Specialist",
    goal="Handle all Resend related tasks and data processing",
    backstory="You are an expert in Resend operations and data analysis",
    tools=resend_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=firecrawl_deep_research_agent,
    markdown=True
)

# Create multi-agent crew
multi_agent_crew = Crew(
    agents=[postgres_agent, firecrawl_deep_research_agent, resend_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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