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name: microsoft-teams-mcp-connector description: "MCP connector for Microsoft Teams – enables AI-powered team collaboration, channel management, and meeting coordination through Claude" license: Proprietary/API tags: ["product","communication","mcp","teams","microsoft"] difficulty: intermediate time_to_master: "4-8 weeks" version: "1.0.0"

Microsoft Teams MCP Connector

Overview

This skill enables Claude to interact with Microsoft Teams through the Model Context Protocol (MCP). It provides a bridge between Claude's AI capabilities and Microsoft Teams's Microsoft Graph API, allowing natural language control of Microsoft Teams operations, intelligent automation, and AI-powered assistance for Microsoft Teams workflows.

When to Use This Skill

• Channel creation and message posting
• Meeting scheduling and agenda creation
• Message summarization across channels
• Adaptive card and bot message creation
• Team membership and permission management

Architecture

┌─────────────┐     ┌─────────────────┐     ┌──────────────────┐
│   Claude     │────▶│   MCP Server    │────▶│   Microsoft Teams   │
│   (Client)   │◀────│   (TypeScript)  │◀────│   (Microsoft Grap)│
└─────────────┘     └─────────────────┘     └──────────────────┘

Core Concepts

MCP Server Setup

The connector implements an MCP server that exposes Microsoft Teams operations as tools Claude can invoke. The server translates natural language intentions into Microsoft Graph API calls.

Key Endpoints/Interfaces

/v1.0/teams, /v1.0/teams/:id/channels, /v1.0/me/chats, /v1.0/me/onlineMeetings

Implementation

// Microsoft Teams MCP Server Implementation
import { McpServer } from "@modelcontextprotocol/sdk/server/mcp.js";
import { StdioServerTransport } from "@modelcontextprotocol/sdk/server/stdio.js";
import { z } from "zod";

const server = new McpServer({
  name: "microsoft-teams-mcp-connector",
  version: "1.0.0",
});

// Tool: List/Query Resources
server.tool(
  "list_resources",
  "List and query Microsoft Teams resources with optional filters",
  {
    query: z.string().optional().describe("Search query or filter"),
    limit: z.number().optional().describe("Max results to return"),
  },
  async ({ query, limit }) => {
    // Call Microsoft Teams Microsoft Graph API
    const response = await fetch(`${BASE_URL}/v1.0/teams`, {
      headers: { "Authorization": `Bearer ${API_KEY}` },
    });
    const data = await response.json();
    return {
      content: [{ type: "text", text: JSON.stringify(data, null, 2) }],
    };
  }
);

// Tool: Create Resource
server.tool(
  "create_resource",
  "Create a new resource in Microsoft Teams",
  {
    name: z.string().describe("Resource name"),
    config: z.object({}).passthrough().optional().describe("Resource configuration"),
  },
  async ({ name, config }) => {
    const response = await fetch(`${BASE_URL}/v1.0/teams`, {
      method: "POST",
      headers: {
        "Authorization": `Bearer ${API_KEY}`,
        "Content-Type": "application/json",
      },
      body: JSON.stringify({ name, ...config }),
    });
    const data = await response.json();
    return {
      content: [{ type: "text", text: `Created: ${JSON.stringify(data)}` }],
    };
  }
);

// Tool: Analyze/Report
server.tool(
  "analyze",
  "AI-powered analysis of Microsoft Teams data",
  {
    type: z.string().describe("Analysis type"),
    timeframe: z.string().optional().describe("Time range for analysis"),
  },
  async ({ type, timeframe }) => {
    // Fetch data and provide AI analysis
    const response = await fetch(`${BASE_URL}/v1.0/teams`, {
      headers: { "Authorization": `Bearer ${API_KEY}` },
    });
    const data = await response.json();
    return {
      content: [{ type: "text", text: JSON.stringify(data, null, 2) }],
    };
  }
);

// Start server
const transport = new StdioServerTransport();
await server.connect(transport);

Claude Desktop Configuration

{
  "mcpServers": {
    "microsoft-teams-mcp-connector": {
      "command": "node",
      "args": ["path/to/microsoft-teams-mcp-connector/index.js"],
      "env": {
        "MICROSOFT_TEAMS_API_KEY": "your-api-key",
        "MICROSOFT_TEAMS_BASE_URL": "https://your-instance-url"
      }
    }
  }
}

Best Practices

1. Authentication: Store API keys securely using environment variables; never hardcode credentials
2. Rate Limiting: Implement request throttling to respect Microsoft Teams API rate limits
3. Error Handling: Provide clear, actionable error messages for common failure scenarios
4. Pagination: Handle paginated responses for large datasets efficiently
5. Caching: Cache frequently accessed read-only data to reduce API calls
6. Security: Validate all inputs before passing to the Microsoft Teams API; sanitize outputs
7. Logging: Log all API interactions for debugging and audit purposes

Example Prompts

"Schedule a team standup for every weekday at 9am and post an agenda template to the channel"

Security Considerations

• All API credentials must be stored as environment variables
• Implement input validation and sanitization for all tool parameters
• Use HTTPS for all API communications
• Follow the principle of least privilege for API token permissions
• Audit log all write operations for compliance tracking

Resources

• Microsoft Teams Official Documentation
• MCP SDK Documentation: https://modelcontextprotocol.io
• MCP Server Examples: https://github.com/modelcontextprotocol/servers
• SkillGalaxy Repository: https://github.com/Sandeeprdy1729/skill_galaxy

Changelog

Version	Date	Changes
1.0.0	2026-04-01	Initial MCP connector skill

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