Connectto WordPress, Resend, Firecrawl Web Search MCP Servers

Create powerful collaborative AI workflows by connecting multiple MCP servers including WordPress, Resend, Firecrawl Web Search for enhanced multi-agent automation capabilities in Klavis AI.

WordPress icon

WordPress

featured

WordPress is an open-source content management system for building websites and blogs

Available Tools:

  • wordpress_create_post
  • wordpress_get_posts
  • wordpress_update_post
  • +4 more tools
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
Firecrawl Web Search icon

Firecrawl Web Search

featured

Advanced web crawling, scraping, and search capabilities powered by Firecrawl

Available Tools:

  • firecrawl_scrape
  • firecrawl_map
  • firecrawl_crawl
  • +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"))

wordpress_mcp_instance = klavis_client.mcp_server.create_server_instance(
    server_name=McpServerName.WORDPRESS,
    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,
)

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

# Initialize MCP tools for each server
wordpress_tools = MCPServerAdapter(wordpress_mcp_instance.server_params)
resend_tools = MCPServerAdapter(resend_mcp_instance.server_params)
firecrawl_web_search_tools = MCPServerAdapter(firecrawl_web_search_mcp_instance.server_params)

# Create specialized agents for each service
wordpress_agent = Agent(
    role="WordPress Specialist",
    goal="Handle all WordPress related tasks and data processing",
    backstory="You are an expert in WordPress operations and data analysis",
    tools=wordpress_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
)

firecrawl_web_search_agent = Agent(
    role="Firecrawl Web Search Specialist",
    goal="Handle all Firecrawl Web Search related tasks and data processing",
    backstory="You are an expert in Firecrawl Web Search operations and data analysis",
    tools=firecrawl_web_search_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=wordpress_agent,
    markdown=True
)

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

# Create multi-agent crew
multi_agent_crew = Crew(
    agents=[wordpress_agent, resend_agent, firecrawl_web_search_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

Ready to Get Started?

Join developers who are already using KlavisAI to power their CrewAI multi-agent systems with these MCP servers.

Start For Free