Can I see if a build pipeline failed via the AI?

Yes! Use the `list_builds` tool and provide the Project ID. Your agent will retrieve the history of recent executions, including their final status (succeeded, failed, inProgress).

How do I list the Git repositories for a project?

Run the `list_repositories` query with your Project ID. The agent will return all Git repositories associated with that project in your Azure DevOps account.

Is it possible to see recent bugs or tasks assigned to a project?

Absolutely. Use the `list_work_items` tool. Your agent will retrieve a list of recent work items, including bugs, tasks, and stories, for the specified project.

INSTANT PLUG AND PLAY

Azure DevOps MCP Server for CrewAIGive CrewAI instant access to 6 tools to List Builds, List Pipelines, List Project Teams, and more

Name: Azure DevOps
Availability: InStock
Author: Vinkius

Built by Vinkius GDPR 6 Tools Framework

Connect your CrewAI agents to Azure DevOps through Vinkius, pass the Edge URL in the `mcps` parameter and every Azure DevOps tool is auto-discovered at runtime. No credentials to manage, no infrastructure to maintain.

Use with Your AI Agent

Ask AI about this App Connector for CrewAI

Open in ChatGPT Open in Claude Open in Perplexity

The Azure DevOps app connector for CrewAI is a standout in the Industry Titans category — giving your AI agent 6 tools to work with, ready to go from day one.

Vinkius delivers Streamable HTTP and SSE to any MCP client

python

from crewai import Agent, Task, Crew

agent = Agent(
    role="Azure DevOps Specialist",
    goal="Help users interact with Azure DevOps effectively",
    backstory=(
        "You are an expert at leveraging Azure DevOps tools "
        "for automation and data analysis."
    ),
    # Your Vinkius token. get it at cloud.vinkius.com
    mcps=["https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"],
)

task = Task(
    description=(
        "Explore all available tools in Azure DevOps "
        "and summarize their capabilities."
    ),
    agent=agent,
    expected_output=(
        "A detailed summary of 6 available tools "
        "and what they can do."
    ),
)

crew = Crew(agents=[agent], tasks=[task])
result = crew.kickoff()
print(result)

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* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure

About Azure DevOps MCP Server

Connect your Azure DevOps account to any AI agent and simplify how you manage your software development lifecycle, track work items, and monitor pipelines through natural conversation.

When paired with CrewAI, Azure DevOps becomes a first-class tool in your multi-agent workflows. Each agent in the crew can call Azure DevOps tools autonomously, one agent queries data, another analyzes results, a third compiles reports, all orchestrated through Vinkius with zero configuration overhead.

What you can do

Project Oversight — List all projects in your organization and retrieve detailed metadata and configurations.
Work Item Tracking — List and query recent tasks, bugs, and user stories to manage your team's backlog.
Git Repository Control — Query all Git repositories within a project to monitor code storage.
Pipeline Monitoring — List CI/CD pipelines and retrieve the history of recent build executions and statuses.
Team Coordination — List project teams to understand organizational structure and distribution.
Operational Status — Fetch real-time metadata for projects and work items directly via AI commands.

The Azure DevOps MCP Server exposes 6 tools through the Vinkius. Connect it to CrewAI in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.

All 6 Azure DevOps tools available for CrewAI

When CrewAI connects to Azure DevOps through Vinkius, your AI agent gets direct access to every tool listed below — spanning ci-cd, pipeline-management, work-item-tracking, and more. Every call is secured with network, filesystem, subprocess, and code evaluation entitlements inside a sandboxed runtime. Beyond a simple connection, you get a full AI Gateway with real-time visibility into agent activity, enterprise governance, and optimized token usage.

list_builds

List recent builds

list_pipelines

List CI/CD pipelines

list_project_teams

List teams in a project

list_projects

List Azure DevOps projects

list_repositories

List Git repositories

list_work_items

List recent work items

Connect Azure DevOps to CrewAI via MCP

Follow these steps to wire Azure DevOps into CrewAI. The entire setup takes under two minutes — your credentials stay safe behind the Vinkius.

Install CrewAI

Run pip install crewai

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token from cloud.vinkius.com

Customize the agent

Adjust the role, goal, and backstory to fit your use case

Run the crew

Run python crew.py. CrewAI auto-discovers 6 tools from Azure DevOps

Why Use CrewAI with the Azure DevOps MCP Server

CrewAI Multi-Agent Orchestration Framework provides unique advantages when paired with Azure DevOps through the Model Context Protocol.

Multi-agent collaboration lets you decompose complex workflows into specialized roles, one agent researches, another analyzes, a third generates reports, each with access to MCP tools

CrewAI's native MCP integration requires zero adapter code: pass Vinkius Edge URL directly in the `mcps` parameter and agents auto-discover every available tool at runtime

Built-in task delegation and shared memory mean agents can pass context between steps without manual state management, enabling multi-hop reasoning across tool calls

Sequential and hierarchical crew patterns map naturally to real-world workflows: enumerate subdomains → analyze DNS history → check WHOIS records → compile findings into actionable reports

Azure DevOps + CrewAI Use Cases

Practical scenarios where CrewAI combined with the Azure DevOps MCP Server delivers measurable value.

Automated multi-step research: a reconnaissance agent queries Azure DevOps for raw data, then a second analyst agent cross-references findings and flags anomalies. all without human handoff

Scheduled intelligence reports: set up a crew that periodically queries Azure DevOps, analyzes trends over time, and generates executive briefings in markdown or PDF format

Multi-source enrichment pipelines: chain Azure DevOps tools with other MCP servers in the same crew, letting agents correlate data across multiple providers in a single workflow

Compliance and audit automation: a compliance agent queries Azure DevOps against predefined policy rules, generates deviation reports, and routes findings to the appropriate team

Example Prompts for Azure DevOps in CrewAI

Ready-to-use prompts you can give your CrewAI agent to start working with Azure DevOps immediately.

"List all active projects in my Azure DevOps organization."

"Show me the last 5 work items for the 'Mobile App' project."

"What is the status of the latest build for project 'Internal Tools'?"

Troubleshooting Azure DevOps MCP Server with CrewAI

Common issues when connecting Azure DevOps to CrewAI through the Vinkius, and how to resolve them.

MCP tools not discovered

Ensure the Edge URL is correct. CrewAI connects lazily when the crew starts. check console output.

Agent not using tools

Make the task description specific. Instead of "do something", say "Use the available tools to list contacts".

Timeout errors

CrewAI has a 10s connection timeout by default. Ensure your network can reach the Edge URL.

Rate limiting or 429 errors

Vinkius enforces per-token rate limits. Check your subscription tier and request quota in the dashboard. Upgrade if you need higher throughput.

Azure DevOps + CrewAI FAQ

Common questions about integrating Azure DevOps MCP Server with CrewAI.

How does CrewAI discover and connect to MCP tools?

CrewAI connects to MCP servers lazily. when the crew starts, each agent resolves its MCP URLs and fetches the tool catalog via the standard tools/list method. This means tools are always fresh and reflect the server's current capabilities. No tool schemas need to be hardcoded.

Can different agents in the same crew use different MCP servers?

Yes. Each agent has its own mcps list, so you can assign specific servers to specific roles. For example, a reconnaissance agent might use a domain intelligence server while an analysis agent uses a vulnerability database server.

What happens when an MCP tool call fails during a crew run?

CrewAI wraps tool failures as context for the agent. The LLM receives the error message and can decide to retry with different parameters, fall back to a different tool, or mark the task as partially complete. This resilience is critical for production workflows.

Can CrewAI agents call multiple MCP tools in parallel?

CrewAI agents execute tool calls sequentially within a single reasoning step. However, you can run multiple agents in parallel using process=Process.parallel, each calling different MCP tools concurrently. This is ideal for workflows where separate data sources need to be queried simultaneously.

Can I run CrewAI crews on a schedule (cron)?

Yes. CrewAI crews are standard Python scripts, so you can invoke them via cron, Airflow, Celery, or any task scheduler. The crew.kickoff() method runs synchronously by default, making it straightforward to integrate into existing pipelines.