I recently set up an MCP server for Azure DevOps – a task that should have been straightforward. It wasn’t. And the way three different AI tools handled the same problem told me more about their underlying philosophies than any benchmark ever could.
The Setup That Broke First
Before any comparison could happen, I hit a wall: an unintuitive npm package error during the MCP configuration. The kind of error where Stack Overflow returns nothing useful and the error message itself gives you no real direction.
That’s when I turned to Cursor.
I described the problem, and Cursor diagnosed it quickly: the npm cache was corrupted. It told me exactly what to do, I ran the fix, and the setup continued. No rabbit hole, no wasted hour – just a working MCP server.
That moment stuck with me. It wasn’t a code generation task. It was debugging an environment issue in a niche context, with little public documentation. And Cursor handled it without drama.
The Actual Test: One Query, Three Tools
With the MCP server running, I ran the same query across all three tools: how many open work items do I have in Azure DevOps?
Simple question. Known answer. A clean benchmark.
Cursor
Response time: roughly ten seconds. Answer: correct. No fuss.
Copilot
Response time: a second or two longer. Answer: also correct. Also no fuss.
Both tools connected to the MCP server, executed the query, and returned the right result. The difference between them was negligible in practice.
Codex
And then there was Codex.
Codex attempted to run the MCP command – and immediately hit a wall. The environment was sandboxed. The MCP server was there, but the sandbox blocked all outbound network traffic. The tool could see the setup, but couldn’t reach the outside world. A lesser tool would have stopped there and returned an error.
Codex did not stop.
Over the next ten minutes, it tried everything: installing Azure DevOps PowerShell modules, writing and executing scripts, finding alternative API paths. It was relentless. Each approach that failed led to another attempt. The work item count it finally returned? Correct.
It took ten minutes instead of ten seconds, never used the MCP server it was supposed to, and found the answer anyway through sheer persistence.
What This Actually Reveals
The easy takeaway is that Cursor and Copilot “won” and Codex “lost”. But I’m not sure that’s the right frame – and I want to be honest about the limits of this comparison.
Cursor and Copilot performed well in a working environment. What I didn’t test is how they would have behaved in Codex’s situation: sandboxed, with MCP inaccessible, forced to improvise. Would they have given up with an error message? Found their own workarounds? I don’t know. The comparison wasn’t fair enough to draw conclusions about resilience.
What I can say is that Codex, at least in this configuration, did not give up when the environment fought back – and that’s a meaningful observation on its own, independent of how the others might have handled it.
Whether that persistence is a strength or a weakness depends on context. In a clean, well-configured dev environment, Codex’s ten-minute detour is overkill. In a constrained or broken environment – legacy systems, air-gapped networks, enterprise setups with limited tooling – that same behavior might be exactly what you need.
What I Learned About MCP
Beyond the tool comparison, the experiment told me something about MCP itself: it works, but the setup experience is still rough enough that a good AI assistant is almost a prerequisite for getting it running. That npm cache issue would have cost me significantly more time without Cursor’s diagnosis.
MCP as a concept – giving AI tools direct access to services like Azure DevOps – has real potential. The gap between tools is not in what they can do once connected, but in how they handle the journey to get there.
Bottom Line
If you’re setting up MCP for Azure DevOps and want a smooth experience, Cursor is currently the strongest companion. Copilot is a solid alternative once the environment is configured. Codex is worth watching – not for speed, but for resilience.
Same task. Three tools. Three very different approaches to getting it done.
Have you compared these tools in your own setup? I’d be curious what your experience looked like – especially in more constrained environments.
