Generate Arm Performix AI insights in Visual Studio Code with Codex
Introduction
Understand the AI-assisted Arm Performix insight workflow
Configure the Arm Performix MCP server in Codex
Create an Arm Performix Code Hotspots run
Generate Arm Performix AI insights for a Code Hotspots run
Open the run in Arm Performix for deeper inspection
Next Steps

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About this Learning Path

Skill level:  Introductory
Reading time:  20 min
Last updated:  27 Jul 2026
Skill level:
  Introductory
Reading time:
  20 min
Last updated:
  27 Jul 2026
Author:Julie Gaskin, Arm
Arm IP:Neoverse
Tags: Performance and Architecture Linux Arm Performix Codex Model Context Protocol Visual Studio Code
Author:
  • Julie Gaskin, Arm
Arm IP:
Neoverse
Tags:
Performance and Architecture Linux Arm Performix Codex Model Context Protocol Visual Studio Code

Who is this for?

This Learning Path is for software developers and performance engineers who want to optimize applications on Arm-based servers using Arm Performix.

What will you learn?

Upon completion of this Learning Path, you will be able to:

  • Configure the Arm Performix MCP server for the Codex extension in VS Code.
  • Verify that Codex can access Arm Performix recipes, targets, and runs.
  • Create or select a supported Code Hotspots run.
  • Generate an AI insight and validate its recommendations against profile evidence.

Prerequisites

Before starting, you will need the following:

  • Arm Performix version 2026.2.5 or later installed. For installation and target setup instructions, see the Arm Performix install guide .
  • Visual Studio Code with the Codex extension installed
  • Access to Codex through ChatGPT sign-in, or an organization-approved OpenAI API key provided through the OPENAI_API_KEY environment variable
  • Permission from your organization to share profile data, symbols, source excerpts, disassembly excerpts, and performance metrics with Codex

Summary

AI-assisted

This summary was drafted with an approved AI-assisted workflow and reviewed by Arm contributors before publication. Human technical review remains part of the process so the final page reflects engineering rigor, accuracy, and Arm editorial standards.

Close
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You’ll learn how to connect Codex in Visual Studio Code to the Arm Performix Model Context Protocol (MCP) server, generate an AI insight for a specific Code Hotspots run, and validate the result with profile evidence. After configuring the MCP server on your local machine, you’ll select or create a representative Code Hotspots run, request an AI insight by run ID, and evaluate its supporting evidence. Then, you’ll open the same run in Arm Performix to inspect flame graphs, functions, call stacks, source, and disassembly to confirm the recommendation.

Frequently asked questions

AI-assisted

These FAQs were drafted with an approved AI-assisted workflow and reviewed by Arm contributors before publication. Human technical review remains part of the process so the final page reflects engineering rigor, accuracy, and Arm editorial standards.

Close
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How do I confirm Codex is connected to Arm Performix through MCP?
Ask Codex to list Arm Performix recipes, targets, or supported Code Hotspots runs. A successful listing confirms the MCP server is configured and reachable.
Which MCP configuration method should I use, and where should I configure it?
You can update Codex extension settings, run the codex mcp add command, or edit ~/.codex/config.toml. Configure the MCP on the host where Codex runs; in remote development, verify which machine hosts Codex.
What should I prepare before creating a new Code Hotspots run?
Choose a representative workload and specify the executable with an absolute path. Build the workload with debug symbols when possible and aim for at least 20 seconds of activity to collect enough samples.
How do I make sure Codex uses the exact profile I want?
Request the AI insight using the run ID. If you don’t know it, ask Codex to list supported Code Hotspots runs with IDs, targets, workloads, and creation times. Then, select the correct ID.
What should I check to validate an AI insight against the profile?
Open the same run in Arm Performix and inspect the Flame graph, Functions, Call Stack, Source, and Disassembly views. Confirm that the cited functions, call paths, and sample percentages match the evidence referenced by the insight.
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