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Azure DevOps MCP Server

by jafarimohammad
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TypeScript
Hybrid

Azure DevOps MCP Server

A Model Context Protocol (MCP) server for Azure DevOps Server 2022 (on-premise) that exposes Azure DevOps capabilities as AI-callable tools — letting any MCP-compatible AI assistant query pipelines, pull requests, builds, and repositories through natural language.

TypeScript Node.js MCP Docker Kubernetes

What can it do?

Ask your AI assistant in plain language — the MCP server handles the Azure DevOps REST API calls:

"What was the last pipeline run? Did it succeed?"
"List all open PRs in project MyProject"
"Which PRs in the last 24 hours have no reviewer assigned?"
"Show me the build logs for the last failed build"
"Run the pipeline named 'my-service [alpha]' on branch develop"
"Read the appsettings.json file from the backend repository"

Related MCP server: Azure DevOps MCP Server

Available Tools

Projects

Tool

Description

list_projects

List all projects in the collection

Repositories & Branches

Tool

Description

list_repositories

List Git repositories in a project

list_branches

List branches of a repository

get_file_content

Read file content from a repository at any branch

Pull Requests

Tool

Description

list_pull_requests

List PRs across all repos in a project (filter by status)

list_prs_without_reviewer

Find open PRs with no reviewer, with optional time window (e.g. last 24 hours)

get_pull_request

Get PR details: description, merge status, reviewers and their votes

create_pull_request

Create a new PR from source to target branch (supports draft)

Pipelines & Builds

Tool

Description

list_pipelines

List all pipeline definitions in a project

get_last_build

Get the most recent build status, optionally filtered by pipeline name

list_builds

List recent builds filtered by pipeline or status

list_failed_builds

Find failed/partial builds in the last N hours

get_build

Get details of a specific build

get_build_logs

Fetch console log output of a build (auto-truncated, last 150 lines)

run_pipeline_by_name

Find and run a pipeline by name (partial match, no ID needed)

run_pipeline

Queue a pipeline run by numeric ID

Architecture

AI Client (Claude, Open WebUI, etc.)
        │  MCP Protocol (JSON-RPC)
        ▼
┌─────────────────────────┐
│   Azure DevOps MCP      │
│   ─────────────────     │
│  HTTP (Kubernetes) or   │
│  stdio (local)          │
│                         │
│  tools/                 │
│    projects.ts          │
│    repos.ts             │
│    pipelines.ts         │
└────────────┬────────────┘
             │  REST API (api-version 7.0)
             │  Basic Auth (PAT)
             ▼
┌─────────────────────────┐
│  Azure DevOps Server    │
│  2022 (on-premise)      │
└─────────────────────────┘

  • Transport: Stateless Streamable HTTP for Kubernetes (scales horizontally, no sticky sessions) or stdio for local use

  • Auth: Personal Access Token via HTTP Basic auth (Authorization: Basic base64(:<PAT>))

  • API version: 7.0 — the highest supported by Azure DevOps Server 2022.0.x

Requirements

  • Node.js 20+

  • Azure DevOps Server 2022 (on-premise)

  • A Personal Access Token with:

    • Code (Read & Write) — for repo, branch, file, and pull request tools

    • Build (Read & Execute) — for pipeline and build tools

Quick Start

Local (stdio — for Claude Desktop / Claude Code)

npm install
npm run build

# Register with Claude Code
claude mcp add azure-devops \
  --env AZDO_ORG_URL=https://your-server.example.com/YourCollection \
  --env AZDO_PAT=your_pat_here \
  --env AZDO_PROJECT=YourProject \
  -- node dist/index.js

Docker

# Build
docker build -t your-registry/azure-devops-mcp:1.0.0 .

# Run locally for testing
docker run -p 3000:3000 \
  -e AZDO_ORG_URL=https://your-server.example.com/YourCollection \
  -e AZDO_PAT=your_pat_here \
  -e AZDO_PROJECT=YourProject \
  your-registry/azure-devops-mcp:1.0.0

Kubernetes

# 1. Create namespace
kubectl create namespace mcp-servers

# 2. Create secret (keep PAT out of git)
kubectl create secret generic azure-devops-mcp-secret \
  --namespace mcp-servers \
  --from-literal=AZDO_PAT='your_pat_here'

# 3. Edit k8s/configmap.yaml with your AZDO_ORG_URL and AZDO_PROJECT

# 4. Apply manifests
kubectl apply -f k8s/configmap.yaml
kubectl apply -f k8s/deployment.yaml
kubectl apply -f k8s/service.yaml

# 5. Verify
kubectl -n mcp-servers rollout status deploy/azure-devops-mcp

In-cluster endpoint:

http://azure-devops-mcp.mcp-servers.svc.cluster.local/mcp

Quick test with port-forward

# Terminal 1
kubectl -n mcp-servers port-forward deploy/azure-devops-mcp 3000:3000

# Terminal 2 — health check
curl http://127.0.0.1:3000/healthz

# Terminal 2 — list projects
curl -sS -X POST http://127.0.0.1:3000/mcp \
  -H "Content-Type: application/json" \
  -H "Accept: application/json, text/event-stream" \
  --data-binary '{"jsonrpc":"2.0","id":1,"method":"tools/call","params":{"name":"list_projects","arguments":{}}}'

Configuration

Environment Variables

Variable

Required

Description

AZDO_ORG_URL

Yes

Collection URL, e.g. https://your-server.example.com/YourCollection

AZDO_PAT

Yes

Personal Access Token

AZDO_PROJECT

No

Default project — avoids passing project name in every request

AZDO_API_VERSION

No

Default: 7.0

MCP_TRANSPORT

No

http for Kubernetes, stdio for local (default: stdio)

PORT

No

HTTP port, default: 3000

MCP_PATH

No

Endpoint path, default: /mcp

.env example

AZDO_ORG_URL=https://your-server.example.com/YourCollection
AZDO_PAT=your_pat_here
AZDO_PROJECT=YourProject
MCP_TRANSPORT=http
PORT=3000

Project Structure

src/
├── index.ts          Entry point — selects transport based on MCP_TRANSPORT
├── config.ts         Env var reading and validation
├── server.ts         MCP server construction and tool registration
├── azureClient.ts    REST client with PAT Basic auth
├── httpServer.ts     Stateless Streamable HTTP transport for Kubernetes
└── tools/
    ├── projects.ts   Project tools
    ├── repos.ts      Repository and pull request tools
    ├── pipelines.ts  Pipeline and build tools
    └── helpers.ts    Shared utility functions

k8s/
├── configmap.yaml    Non-secret configuration
├── deployment.yaml   Kubernetes Deployment (non-root, read-only FS)
└── service.yaml      ClusterIP Service

Technical Notes

  • Stateless HTTP: Each request creates an independent MCP server instance — scales horizontally without sticky sessions.

  • Health check: GET /healthz for Kubernetes liveness and readiness probes.

  • API version 7.0: Azure DevOps Server 2022.0.x supports up to 7.0 only. Version 7.1 is available in Azure DevOps Services (cloud) and Server 2022.1+.

  • Container security: runs as non-root user 1000, read-only root filesystem, all Linux capabilities dropped.

  • Weak model friendly: Tools return pre-processed, ready-to-answer data rather than raw JSON — works well with smaller models that struggle to process large API responses.

License

MIT

Install Server
A
license - permissive license
A
quality
B
maintenance

How are these scores calculated?

Maintenance

–Maintainers
–Response time
–Release cycle
–Releases (12mo)
Commit activity

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