Azure DevOps MCP Server
Enables AI agents to query and interact with Azure DevOps data, including work items, projects, ticket statistics, and backlog information through natural language commands.
README
🚀 Azure DevOps MCP Server
A custom Model Context Protocol (MCP) server built with NestJS, enabling AI agents (like Claude Desktop) to securely query Azure DevOps data such as work items, projects, and ticket statistics.
📌 Overview
This project exposes Azure DevOps data through a standards-based MCP server, allowing any MCP-compatible AI agent to perform real-time queries such as:
- 🔍 List all projects in the organization
- 📝 Get count of created / active / closed work items
- 🧩 Fetch ticket details
- 📊 Query backlog items or sprints
- 🏗️ Extend Azure DevOps automation through AI
It supports two modes:
1️⃣ STDIO MCP Server
Tested locally using the MCP Inspector (not using a direct Claude Desktop STDIO client).
2️⃣ HTTP MCP Server
Allows remote access using MCP-over-HTTP.
🏗️ Architecture
NestJS Application
│
├── MCP Module
│ ├── Tool Definitions
│ ├── STDIO Transport
│ └── HTTP Transport
│
├── Azure DevOps Service
│ └── Work Item API
│ └── Project API
│
└── Utilities
├── MCP Message Handlers
└── Logger
✨ Features
✔️ Custom Tools for Azure DevOps
Tools exposed to MCP clients:
| Tool Name | Description |
|---|---|
list_projects |
Returns all Azure DevOps projects |
count_tickets |
Returns summary of created/active/resolved tickets |
get_workitem |
Fetch details of a single work item |
search_workitems |
Query work items based on filters |
✔️ STDIO Transport (MCP Inspector)
This project was tested locally using the MCP Inspector, which acts as an MCP client that connects over STDIO to your server.
Run the server so the inspector can connect over STDIO:
node dist/main.js
Start the inspector with:
npx @modelcontextprotocol/inspector
When prompted in the inspector, choose the STDIO connection and point it at the running process (the inspector will spawn or attach to the process as configured).
✔️ HTTP MCP Support
Start server:
npm run start:prod
You can POST MCP-style requests:
POST http://localhost:3000/mcp
🔧 Installation
git clone https://github.com/saktheeswar/Azure_Devops_MCP_Server.git
cd azure-devops-mcp
npm install
npm run build
🔑 Environment Variables
Create .env:
BASE_URL=url
AUSER_NAME=your-personal-access-token
ADO_PAT=your-default-project
▶️ Running the Server
Run in STDIO mode (for MCP Inspector)
node dist/main.js
Run in HTTP mode
npm run start:prod
🧪 Testing with MCP Inspector
(You will explain this with screenshots in your blog.)
npx @modelcontextprotocol/inspector
Then connect using STDIO:
node dist/main.js
💻 Code Walkthrough
📁 MCP Server Setup
const server = new Server({
name: "azure-devops-mcp",
version: "1.0.0",
tools: {
list_projects: { ... },
count_tickets: { ... }
}
});
📁 Azure DevOps API Integration
async getProjects() {
const url = `${this.baseUrl}/projects?api-version=7.0`;
return this.http.get(url, this.headers);
}
📁 STDIO Bootstrap
bootstrapStdio(server);
📁 HTTP Controller
@Post('/mcp')
handleMcp(@Body() body) {
return this.mcpHttpService.process(body);
}
📚 Example MCP Tool Call
{
"method": "tools/list_projects",
"params": {}
}
Response:
{
"projects": [
{ "name": "Frontend" },
{ "name": "Backend" },
{ "name": "Infrastructure" }
]
}
🧩 Use Cases
For Developers
- Query Azure DevOps without opening the UI
- Get ticket summaries instantly
- Automate repetitive DevOps activities
For AI Assistants
- Smart sprint planning
- Ticket prioritization
- Automated status reporting
For Teams
- Faster decision making
- AI-powered insights
🔥 Future Enhancements
- Create/Update work items using AI
- Integration with Release Pipelines
- Sprint burndown insights
- PR and Repository analytics
📎 Screenshots (Add your own)
- Claude Desktop working
- MCP Inspector connected
- Tool response logs
🧑💻 Author
Saktheeswaran M AI Engineer & Full-Stack Developer
🔗 Source Code
👉 GitHub Repository: (https://github.com/saktheeswar/Azure_Devops_MCP_Server)
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