Azure DevOps MCP for AI. Track builds and manage work items with conversation.
Works with every AI agent you already use
…and any MCP-compatible client








Connect to your AI in seconds.
Azure DevOps MCP connects your AI agent directly to your entire development ecosystem. Manage work items, track build pipelines, and map out project structures in one conversation.
You can list all projects, query recent tasks, check code repositories, and monitor CI/CD pipeline history without clicking through dashboards or digging up IDs.
It gives you full visibility into the software development lifecycle right where you're working.
What your AI can do
List builds
Retrieves a list of recent Continuous Integration/Continuous Deployment build executions.
List pipelines
Provides a list of all configured CI/CD pipelines within the organization.
List projects
Fetches an inventory of every active project in your Azure DevOps account.
List every active project in the organization to understand which teams are working on what.
Query recent work items, like user stories or specific bugs, to review team backlogs.
Get a list of all Git repositories within an active project for quick access to code locations.
List CI/CD pipelines and pull the execution history, seeing if builds succeeded or failed.
View which project teams exist to understand who is responsible for different parts of the work.
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Azure DevOps: 6 Tools for Development Management
These tools let your agent interact with core functions like listing projects, tracking work items, and monitoring build pipelines in a structured way.
Make your AI actually useful.
Add this MCP to Claude, Cursor, or Windsurf and your AI stops guessing. It gets real tools to look things up, take action, and handle the stuff you keep doing by hand.
Start using Azure DevOps on VinkiusList Builds
Retrieves a list of recent Continuous Integration/Continuous Deployment build executions.
List Pipelines
Provides a list of all configured CI/CD pipelines within the organization.
List Projects
Fetches an inventory of every active project in your Azure DevOps account.
List Repositories
Lists all code repositories associated with a specific project.
List Project Teams
Displays the teams organized within a given project structure.
List Work Items
Queries and lists recent tasks, bugs, or user stories from the team backlog.
Security and governance baked right in.
Pick your AI client below to get set up. Just create a Vinkius account, subscribe, and you're instantly up and running. We handle the entire backend infrastructure, delivering out-of-the-box support for HTTPS Streamable, SSE, and OAuth2—zero messy routing required.
Choose How to Get Started
Build a custom MCP for your own tools, or connect a ready-made integration from our catalog.
Build Your Own
Turn any API into an MCP. Import a spec, define Agent Skills, or deploy with MCPFusion.
- Import from OpenAPI, Swagger, or YAML specs
- Create Agent Skills with progressive disclosure
- Deploy to edge with MCPFusion framework
- Built in DLP, auth, and compliance on every call
- Real time usage dashboard and cost metering
- Publish to catalog or keep private
Make Your AI Do More
Start with Azure DevOps, then connect any of our 5,100+ other servers whenever your AI needs more. One click, no limits.
- Use this MCP plus 5,100+ others, all in one place
- Add new capabilities to your AI anytime you want
- Every connection is secured and compliant automatically
- Track usage and costs across all your servers
- Works with Claude, ChatGPT, Cursor, and more
- New servers added to the catalog every week
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Works with Claude, ChatGPT, Cursor, and more
The Model Context Protocol standardizes how applications expose capabilities to LLMs. Instead of operating in isolation, your AI gains direct access to external platforms, live data, and real-world actions through secure, standardized connections.
This connection provides 6 powerful capabilities that interface natively with Claude, ChatGPT, Cursor, and other compatible AI platforms. No middleware. No custom integration required.
The Dev Cycle Dashboard Nightmare
Today, getting a full picture of what's happening—from feature requirements to deployed code—means opening five different browser tabs. You click into the 'Boards' section to check user stories; then you jump to 'Repos' to see if the right branch exists; finally, you navigate to 'Pipelines' just to see if the last build actually worked. This is manual context switching that kills momentum.
With this MCP, your agent acts as a single pane of glass for everything. You tell it what you need—like checking project health or listing recent tasks—and all the necessary information flows back in one clean answer. It turns hours of dashboard clicking into a few natural sentences.
Azure DevOps MCP: Build Status, Work Items, and Repositories
You stop manually querying project metadata by listing projects or fetching the status of builds. You don't have to remember which ID belongs to which pipeline; you just ask about it.
The result is simple: your agent provides a unified, immediate narrative of your entire development process. Nothing is left out.
What your AI can actually do with this
Managing a large-scale software project means juggling dozens of tools—Jira for stories, Git for code, Azure Portal for pipelines. This MCP lets your agent handle that coordination through natural language conversation. You simply ask questions about your development process, and it retrieves the necessary data directly from your organization. For example, you can list all projects in scope or check the status of a specific build without ever needing to manually enter an ID.
If you're looking for centralized control over your entire DevOps workflow, Vinkius hosts this MCP so you connect once and access full pipeline visibility across any compatible client.
019dd0c0-0d4b-7356-9046-d453ae059986 Here's how it actually works
The bottom line is you treat your complex development dashboard like a chat window, letting your AI client do all the data fetching for you.
Subscribe to this MCP and provide your Azure DevOps Organization details along with a Personal Access Token (PAT).
Your AI client uses these credentials to establish read-only access across the entire DevOps workspace.
You prompt your agent using plain English questions, and it executes the necessary commands to retrieve project metadata or build logs.
Who is this actually for?
Product Owners and DevOps Specialists who are tired of clicking through five different dashboards to get one status report. If you spend more time finding information than actually building software, this MCP saves hours.
Monitors build history and manages repository access directly from the workspace chat.
Gets instant, high-level views of project health by querying work item progress across multiple projects.
Quickly verifies pipeline statuses or checks detailed work item requirements without leaving their coding environment.
What Changes When You Connect
You get real-time build status updates. Instead of navigating to the Pipelines tab, you just ask your agent to list builds, which immediately shows the success or failure status for recent deployments.
Managing backlogs becomes instant. Use the ability to list work items so you can pull up the latest bugs and user stories without manually filtering huge spreadsheets.
Know exactly where the code lives. Query all Git repositories in a project with one command, giving you an immediate map of your entire codebase structure.
Understand organizational roles better. You can list project teams to see who owns which part of the system, helping coordinate tasks across departments quickly.
Centralized visibility saves time. Instead of opening multiple dashboards (projects, builds, work items), your agent brings all that metadata together into one conversational output.
See it in action
Finding the cause of a recent failure
A developer notices a deployment failed. Instead of logging into three different consoles, they ask their agent to check the build history for that project. The agent uses list_builds and identifies the exact pipeline that failed and when it happened.
Prioritizing the next development sprint
A Product Owner needs to know what work is ready. They ask the agent to list work items for a project, which instantly provides five recent stories and three high-priority bugs, allowing them to finalize the sprint plan immediately.
Onboarding a new team member
A new engineer needs an overview of the current system. They ask the agent to list projects and then list repositories for the most critical project, giving the newcomer a complete map of code locations in minutes.
Auditing project scope changes
An ops specialist suspects unauthorized work. They use the tool to list all active projects and check which teams are assigned to each one, immediately flagging any unexpected or unmanaged departmental additions.
The honest tradeoffs
Manual Status Checks
Opening the Azure DevOps portal, navigating to 'Boards', then filtering by date range and status. This takes 10-15 minutes of clicking and copying IDs.
Just ask your agent: 'List recent work items for Project Alpha.' The agent handles the navigation and data retrieval using list_work_items in seconds.
Lost Code Location
Remembering that a specific microservice code lives somewhere between three different Git repositories, requiring manual browsing through project settings.
Ask your agent to 'List all Git repositories for the Core Platform project.' It provides an immediate list of every necessary repository name.
Build Tracking Overload
Trying to figure out which build succeeded last week, requiring checking multiple pipeline views and status codes.
Ask your agent: 'Show me the latest three builds for Project Beta.' It uses list_builds to give a summarized history immediately.
When It Fits, When It Doesn't
Use this MCP if your job requires constant cross-referencing between different parts of your software development lifecycle—specifically linking work item requirements (backlogs) to code repositories and deployed pipelines. If you need to know 'What is the status of X?', this tool works. Don't use it, however, if your only goal is simple chat or basic text generation. For those tasks, a general-purpose agent will suffice. You don't need this MCP just because you want an AI assistant; you need it because you are building complex workflows that span multiple Azure DevOps services. If you can solve the problem by simply listing projects and tracking work items, then this is your tool.
Questions you might have
How do I use the Azure DevOps MCP to check if my code built successfully? +
You ask your agent to list builds for a project, and it returns the status of recent deployments. This uses the list_builds tool, giving you immediate success or failure feedback.
Can I use the Azure DevOps MCP to see all my projects? +
Yes, simply ask your agent to list all active projects in the organization. The agent runs list_projects and gives you a full inventory of what's running.
What if I need to check a specific bug ticket using Azure DevOps MCP? +
You use the capability that queries recent work items. This action calls list_work_items, allowing you to pull up details on any story or bug by name or ID.
Does the Azure DevOps MCP help with Git repositories? +
Yes, it provides a tool to list all Git repositories within a project. This helps developers quickly confirm where specific code segments are stored.
How do I know what tools are available in the Azure DevOps MCP? +
The MCP exposes several operational tools, including list_pipelines for build monitoring and list_project_teams to understand who owns which part of the work.
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