Connectto Jira, Firecrawl Deep Research, Klavis ReportGen MCP Servers

Create powerful collaborative AI workflows by connecting multiple MCP servers including Jira, Firecrawl Deep Research, Klavis ReportGen for enhanced multi-agent automation capabilities in Klavis AI.

Jira icon

Jira

featured

Jira is a project management and issue tracking tool developed by Atlassian

Available Tools:

  • jira_search
  • jira_get_issue
  • jira_search_fields
  • +11 more tools
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
Klavis ReportGen icon

Klavis ReportGen

featured

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

Available Tools:

  • generate_web_reports

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"))

jira_mcp_instance = klavis_client.mcp_server.create_server_instance(
    server_name=McpServerName.JIRA,
    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,
)

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,
)

# Initialize MCP tools for each server
jira_tools = MCPServerAdapter(jira_mcp_instance.server_params)
firecrawl_deep_research_tools = MCPServerAdapter(firecrawl_deep_research_mcp_instance.server_params)
klavis_reportgen_tools = MCPServerAdapter(klavis_reportgen_mcp_instance.server_params)

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

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
)

# 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=jira_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=[jira_agent, firecrawl_deep_research_agent, klavis_reportgen_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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