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title Enable AI assistance with the Azure DevOps MCP Server
titleSuffix Azure Boards
description Learn about the Azure DevOps Model Context Protocol (MCP) Server, which enhances your AI assistant with real-time Azure DevOps context for smarter, more accurate project insights and decision-making capabilities.
ms.service azure-devops-boards
ms.custom peer-review-program
ms.collection ce-skilling-ai-copilot
ms.update-cycle 180-days
ms.topic overview
ms.author chcomley
author chcomley
monikerRange azure-devops
ms.date 12/11/2025

Enable AI assistance with Azure DevOps MCP Server

[!INCLUDE version-eq-azure-devops]

Consider asking your AI assistant "Get my current sprint work items, then identify which ones might be at risk" and getting instant access to your actual Azure DevOps data. The Azure DevOps Model Context Protocol (MCP) Server provides your AI assistant with secure access to work items, pull requests, builds, test plans, and documentation from your Azure DevOps organization.

Unlike cloud-based solutions that require sending your data externally, the Azure DevOps MCP Server runs locally within your secure environment, ensuring your sensitive project information never leaves your network while still delivering enterprise-grade AI capabilities.

Important

  • The Azure DevOps MCP Server is free to use. However, standard Azure DevOps pricing applies to your organization and any data access through the service. AI assistant usage might have separate costs depending on your chosen AI platform.
  • The Azure DevOps MCP Server requires your AI assistant to operate in agent-mode to access Azure DevOps data and perform operations.

Prerequisites

System requirements: Node.js 18.0+ and an active Azure DevOps organization

Install Azure DevOps MCP Server

The Azure DevOps MCP Server integrates with various development environments and AI assistants. Choose your preferred environment for instructions. The prerequisites listed in the table are environment-specific requirements in addition to the system requirements previously listed.

Environment Prerequisites Installation Features
Visual Studio Code (recommended) GitHub Copilot or Claude Dev extension One-click install Extensive MCP support with multiple AI assistant options
Visual Studio (2022 and later) GitHub Copilot component Visual Studio setup guide Full IntelliSense integration with Azure DevOps data
Cursor Built-in AI assistant (no extensions needed) Cursor setup guide Native MCP integration
Claude Desktop Claude Desktop application Claude Desktop setup guide Standalone application with full Azure DevOps integration
JetBrains IDEs Compatible AI assistant plugin JetBrains setup guide IDE-specific integration via plugins
Other environments Varies by environment Azure DevOps MCP Server docs repository See repository for all options

Tip

Having installation issues? Check the troubleshooting section or report issues on the Azure DevOps MCP Server GitHub repository.

Why use Azure DevOps MCP Server?

Traditional AI assistants lack context about your specific projects, work items, and team processes. They can help with generic coding questions but can't answer "What's blocking our current sprint?" or "Which pull requests need my review?" The Azure DevOps MCP Server bridges this gap by connecting your AI assistant directly to your Azure DevOps data.

The Azure DevOps MCP Server provides contextual intelligence based on your actual project data, not generic responses. You can ask natural language questions about your work items, sprints, and releases, and receive insights that understand your team's specific processes and terminology. This process eliminates context switching between tools, provides instant answers without navigating through the Azure DevOps web interface, and automates routine project management tasks through natural language.

Security and privacy

The Azure DevOps MCP Server prioritizes data security and privacy:

  • Local execution: No data leaves your environment - everything runs locally within your secure network
  • No external API calls: The server doesn't make external API calls that could expose sensitive project information
  • User control: You maintain full control over what data your AI assistant can access
  • Secure integration: Works seamlessly with your existing AI coding environments without compromising security
  • Private data handling: Your sensitive project information never leaves your network while still delivering enterprise-grade AI capabilities

What does MCP Server do?

The Azure DevOps MCP Server enables a two-step process: data retrieval and AI analysis.

1. Data retrieval (MCP Server)

The server provides secure access to your Azure DevOps data:

  • Projects and teams: Organization structure and team information
  • Work items: Assigned tasks, bugs, user stories, and their details
  • Pull requests: Code review status, changes, and linked work items
  • Builds and pipelines: CI/CD status, test results, and deployment information
  • Test plans: Test cases, results, and coverage data

2. AI analysis (Your AI assistant)

Your AI assistant processes this data to provide:

  • Intelligent summaries: Sprint progress, team velocity, and project health
  • Actionable insights: Risk identification, bottleneck analysis, and recommendations
  • Context-aware responses: Answers that understand your specific projects and processes

Example: You ask "What's blocking our sprint?" → MCP Server retrieves your current work items → Your AI assistant analyzes the data and identifies specific blockers with recommendations.

Common use cases and examples

For more examples, see Example usage.

Tip

To avoid using stale or cached data from previous queries, add to your prompt, "Do not use previously fetched data."

Daily standup preparation

:::row::: :::column span="1"::: Component :::column-end::: :::column span="2"::: Description :::column-end::: :::row-end:::

:::row::: :::column span="1"::: MCP Server provides :::column-end::: :::column span="2"::: Access to your assigned work items and recent updates :::column-end::: :::row-end:::

:::row::: :::column span="1"::: AI assistant action :::column-end::: :::column span="2"::: Analyzes the data to help you prepare for standup :::column-end::: :::row-end:::

:::row::: :::column span="1"::: Example prompt :::column-end::: :::column span="2"::: "Get my work items for project MyProject and help me prepare for standup - what did I complete, what am I working on, and what's blocked?" :::column-end::: :::row-end:::

:::row::: :::column span="1"::: Query translation :::column-end::: :::column span="2"::: WIQL query to retrieve your assigned work items, filtered by project and excluding removed items, ordered by most recent changes :::column-end::: :::row-end:::

:::row::: :::column span="1"::: Raw data result :::column-end::: :::column span="2"::: Six work items including bugs, tasks, user stories, and features with their current states (Resolved, Done, Active, New, Blocked) :::column-end::: :::row-end:::

:::row::: :::column span="1"::: AI assistant output :::column-end::: :::column span="2"::: | Status | Work Items | |--------|------------| | ✅ Completed | Bug #4521: "Sign in page crashes on mobile Safari" - Resolved yesterday
Task #4498: "Update API documentation for v2.1" - Completed 2 days ago | | 🔄 In Progress | User Story #4456: "Implement two-factor authentication" - In Progress (three days)
└── Subtasks: Database schema (Done), UI components (In Progress), Testing (Not Started)

Bug #4534: "Performance issue with large datasets" - Active (one day) | | ⚠️ Blocked | Feature #4423: "Integration with external payment gateway" - Blocked
└── Waiting for: Security team approval (blocked five days)
└── Action needed: Follow up with Sarah from InfoSec team
Task #4467: "Deploy to staging environment" - Impediment
└── Issue: Staging server maintenance scheduled today 2-4 PM | |Summary for standup| - Yesterday: Resolved mobile Safari sign-in issue, completed API docs.
- Today: Continue 2FA implementation, investigate dataset performance issue.
- Blockers: Need security approval for payment integration, staging deployment delayed due to maintenance.| :::column-end::: :::row-end:::

Sprint planning support

:::row::: :::column span="1"::: Component :::column-end::: :::column span="2"::: Description :::column-end::: :::row-end:::

:::row::: :::column span="1"::: MCP Server provides :::column-end::: :::column span="2"::: Access to backlog items, team data, and work item details :::column-end::: :::row-end:::

:::row::: :::column span="1"::: AI assistant action :::column-end::: :::column span="2"::: Helps analyze capacity and plan the sprint :::column-end::: :::row-end:::

:::row::: :::column span="1"::: Example prompt :::column-end::: :::column span="2"::: "Get all work items in the product backlog for MyProject and prioritize them for a two-week sprint with three developers" :::column-end::: :::row-end:::

:::row::: :::column span="1"::: Query translation :::column-end::: :::column span="2"::: WIQL query to retrieve backlog items with story points, priority, and state information, ordered by priority and stack rank :::column-end::: :::row-end:::

:::row::: :::column span="1"::: Raw data result :::column-end::: :::column span="2"::: Nine backlog items including epics, features, user stories, bugs, and tasks with story points and dependencies, plus team metrics (three developers, 14-day sprint, 60-point velocity) :::column-end::: :::row-end:::

:::row::: :::column span="1"::: AI assistant output :::column-end::: :::column span="2"::: |Category | Work Items | Points | |----------|------------|--------| | ✅ Recommended | Epic #3401: "User Profile Enhancement" - High business value, no dependencies
Bug #3456: "Search results time out on large datasets" - Critical user impact
Feature #3378: "Export dashboard to PDF" - Frequently requested
Story #3489: "Implement password reset flow" - Security priority
Task #3512: "Upgrade React components to v18" - Technical debt
Story #3467: "Add dark mode toggle" - Quick win | 60 total
(13 + 8 + 13 + 8 + 13 + 5) | | ⏳ Deferred | Epic #3234: "Mobile app rewrite" - Too large for single sprint
Feature #3445: "AI-powered recommendations" - Waiting for data science team
Story #3523: "Integration with external CRM" - Blocked by vendor API | 34, 21, 13 | |Planning recommendations|- Balance: Mix of features (3), bugs (1), technical debt (1), quick wins (1)
- Risk level: Low-medium risk items for predictable delivery
- Dependencies: All selected items have resolved dependencies
- Skills: Work distribution matches current team expertise
- Business value: Prioritized customer-facing features and critical fixes| :::column-end::: :::row-end:::

Code review workflow

:::row::: :::column span="1"::: Component :::column-end::: :::column span="2"::: Description :::column-end::: :::row-end:::

:::row::: :::column span="1"::: MCP Server provides :::column-end::: :::column span="2"::: Pull request details and linked work item information :::column-end::: :::row-end:::

:::row::: :::column span="1"::: AI assistant action :::column-end::: :::column span="2"::: Provides business context for technical changes :::column-end::: :::row-end:::

:::row::: :::column span="1"::: Example prompt :::column-end::: :::column span="2"::: "Get details for pull request #67 and its linked work items and help me understand the business effect of these code changes" :::column-end::: :::row-end:::

:::row::: :::column span="1"::: API translation :::column-end::: :::column span="2"::: REST API calls to retrieve pull request details and linked work item information with expanded relations :::column-end::: :::row-end:::

:::row::: :::column span="1"::: Raw data result :::column-end::: :::column span="2"::: Pull request #67 (caching layer implementation, eight files changed, +156/-42 lines) and two linked work items (performance user story and settings bug) :::column-end::: :::row-end:::

:::row::: :::column span="1"::: AI assistant output :::column-end::: :::column span="2"::: | Component | Details | |-----------|---------| | Pull Request | PR #67: "Implement caching layer for user preferences"
Author: Christie Church, Status: Active (awaiting review)
Files: 8 changed (+156, -42 lines), Created: Two days ago | | Linked Work Items | 🎯 User Story #2847: "Improve app performance for returning users" (High priority)
└── Target: Page load time < 2 seconds for logged-in users

🐛 Bug #2901: "Settings page loads slowly for users with many preferences" (Medium severity)
└── Impact: Affects 15% of premium users, causing support tickets | |Business impact summary| - 💼 Strategic Alignment: Supports Q3 user retention goal (20% improvement)
Addresses premium user experience issues affecting revenue
Enables future personalization features
- 📊 Expected Outcomes: Performance: 60% reduction in preference loading time (2.5s → 1.0s)
User Experience: Eliminates loading delays in settings and profile pages
Support Impact: Expected 40% reduction in performance-related tickets
- 🔍 Review Focus Areas: Cache invalidation logic (data consistency)
Error handling for cache unavailability
Performance monitoring implementation
Security considerations for cached user data | :::column-end::: :::row-end:::