Azure DevOps MCP Server

A production-ready Model Context Protocol (MCP) server that enables AI assistants like Claude to interact with Azure DevOps. This server provides tools for managing work items, repositories, wikis, and automating development workflows.

✨ Features

🎫 Work Item Management

• Get Work Items: Fetch work items by ID with full details
• Query Work Items: Use WIQL or simple filters to find work items
• Create Work Items: Create Tasks, Bugs, PBIs, Features, and Epics
• Update Work Items: Modify state, description, assignments, and more

📁 Repository Operations

• List Repositories: Get all repos in a project
• Analyze Repositories: Detect tech stack, frameworks, and structure
• Read Files: Get file content from any branch
• Browse Structure: Navigate the folder hierarchy

📚 Wiki Management

• List Wikis: Get all wikis in a project
• Read Pages: Fetch wiki page content
• Create/Update Pages: Manage wiki documentation

🤖 AI Workflow Automation

• Analyze Work Items: Extract requirements and technical hints
• Suggest Repositories: Match work items to appropriate repos
• Generate Plans: Create implementation plans from work items
• Track Progress: Update work items with progress

🚀 Quick Start

Prerequisites

• Python 3.10 or higher
• Azure DevOps account with a Personal Access Token (PAT)

Installation

# Clone or navigate to the project
cd ado-mcp

# Create virtual environment
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

# Install the package
pip install -e .

Configuration

Create a .env file in the project root:

# Copy the example
cp .env.example .env

# Edit with your values
ADO_ORGANIZATION_URL=https://dev.azure.com/YourOrganization
ADO_PAT=your_personal_access_token
ADO_DEFAULT_PROJECT=YourDefaultProject  # Optional
LOG_LEVEL=INFO

#### Required PAT Scopes

Your Personal Access Token needs these permissions:

• Work Items: Read & Write
• Code (Repositories): Read & Write
• Wiki: Read & Write
• Project and Team: Read

Running the Server

# Run directly
python -m ado_mcp.server

# Or use the CLI command
ado-mcp

🔧 Integration with AI Assistants

Claude Desktop

Add to ~/Library/Application Support/Claude/claude_desktop_config.json (macOS) or %APPDATA%\Claude\claude_desktop_config.json (Windows):

{
  "mcpServers": {
    "azure-devops": {
      "command": "python",
      "args": ["-m", "ado_mcp.server"],
      "cwd": "/path/to/ado-mcp",
      "env": {
        "ADO_ORGANIZATION_URL": "https://dev.azure.com/YourOrg",
        "ADO_PAT": "your_pat_here",
        "ADO_DEFAULT_PROJECT": "YourProject"
      }
    }
  }
}

VS Code with Copilot

Configure in your VS Code settings or workspace configuration.

📖 Usage Examples

Once connected, you can ask the AI assistant:

Work Items

"Get details of work item #1234"
"Show me all active tasks assigned to me"
"Create a new bug: Login button not working on mobile"
"Update work item #1234 to Resolved with comment 'Fixed in PR #567'"

Repositories

"List all repositories in the project"
"Analyze the backend-api repository"
"Show me the folder structure of frontend-app"
"Get the content of /src/main.py from backend-api"

Wiki

"List all wikis in the project"
"Get the content of the /Home wiki page"
"Create a wiki page for the new authentication feature"

AI Workflows

"I want to work on PBI #1234, analyze it and suggest a repository"
"Generate an implementation plan for task #5678"
"Update work item #1234 with 50% progress"

🛠️ Development

Running Tests

# Install dev dependencies
pip install -e ".[dev]"

# Run tests
pytest tests/ -v

# With coverage
pytest tests/ -v --cov=src/ado_mcp

Code Quality

# Lint and format
ruff check src/
ruff format src/

# Type checking
mypy src/

📁 Project Structure

ado-mcp/
├── src/ado_mcp/
│   ├── __init__.py
│   ├── server.py           # MCP server entry point
│   ├── config.py           # Configuration management
│   ├── ado_client.py       # Azure DevOps API client
│   ├── tools/              # MCP Tools
│   │   ├── work_items.py   # Work item operations
│   │   ├── repositories.py # Repository operations
│   │   ├── wiki.py         # Wiki operations
│   │   └── analysis.py     # AI workflow analysis
│   ├── resources/          # MCP Resources
│   │   └── projects.py     # Project resources
│   └── prompts/            # MCP Prompts
│       └── workflows.py    # Workflow templates
├── tests/                  # Test files
├── pyproject.toml          # Project configuration
├── .env.example            # Environment template
└── README.md               # This file

🔒 Security

• Local Execution: The server runs locally, keeping your PAT secure
• No External Calls: All communication is with your Azure DevOps instance
• Environment Variables: Credentials stored in environment, not code

📜 License

MIT License - see LICENSE file for details.

🤝 Contributing

Contributions are welcome! Please read the contributing guidelines before submitting PRs.

Built with ❤️ for the Azure DevOps and AI community