Connectto Microsoft Teams MCP Server

Seamlessly integrate your CrewAI multi-agent systems with Microsoft Teams using Klavis AI's comprehensive MCP server connection guide.

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Microsoft Teams

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Microsoft Teams is a collaboration platform that combines workplace chat, meetings, calling, and file sharing. Manage teams, channels, messages, meetings, and collaborate with your organization seamlessly

Quick Setup Guide

Follow these steps to connect CrewAI to this MCP server

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 the MCP server 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"))

microsoft_teams_mcp_instance = klavis_client.mcp_server.create_server_instance(
    server_name=McpServerName.MICROSOFT_TEAMS,
    user_id="1234",
    connection_type=ConnectionType.STREAMABLE_HTTP,
)

with MCPServerAdapter(microsoft_teams_mcp_instance.server_params) as mcp_tools:
    # Create a Microsoft Teams Analysis Agent
    microsoft_teams_agent = Agent(
        role="Microsoft Teams Analyst",
        goal="Research and analyze microsoft teams to extract comprehensive insights",
        backstory="You are an expert at analyzing microsoft teams and creating professional summaries.",
        tools=mcp_tools,
        reasoning=True,
        verbose=False
    )
    
    # Define Task
    analysis_task = Task(
        description=f"Research and analyze microsoft teams data. Extract relevant information and create a comprehensive summary with key points and main takeaways.",
        expected_output="Complete analysis with structured summary, key insights, and main takeaways",
        agent=microsoft_teams_agent,
        markdown=True
    )
    
    # Create and execute the crew
    microsoft_teams_crew = Crew(
        agents=[microsoft_teams_agent],
        tasks=[analysis_task],
        verbose=False,
        process=Process.sequential
    )
    
    result = microsoft_teams_crew.kickoff()

Frequently Asked Questions

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