Uniprof

Name: Uniprof
Author: indragiek

by indragiekversion 0.3.4i★ –iRuns on your computer

Registry nameio.github.indragiek/uniprof

Install

Overview

Description from the maker (original English)

Universal CPU profiler designed for humans and AI agents

PopNx MCP’s three pillars

These three are never merged into one score: opinions, measurements and popularity are different things.

User voiceiNo reviews yetReviews open together with accounts in a later step
Our basic checksi1 of 1 passedChecked from public information; code not scanned yet
UsageiNot measured yet★ –

What you can do with it

We have not written a plain-language summary for this one yet. Below are the facts we read from the registry.

Connection: Runs on your computer
Transport: stdio
Category: Other

Install

Pick the AI app you use, then copy the settings.

Before you install

Basic checks: 1 of 1 passed
Runs on your computer with your user’s permissions (files, network, apps).
The registry lists no key to fill in.

You need

npmNode.js installed (for the npx command)

Which app do you use?

ClaudeClaude CodeCursorVS CodeChatGPT

Pick your app above. We remember it, so next time the button says “Install in …”.

Open Claude Desktop → Settings → Developer → Edit Config, and paste this into claude_desktop_config.json

{
  "mcpServers": {
    "uniprof": {
      "command": "npx",
      "args": [
        "-y",
        "uniprof",
        "mcp",
        "run"
      ]
    }
  }
}

Run this in your terminal

claude mcp add uniprof -- npx -y uniprof mcp run

Open in Cursor

Or copy the settings yourself

Paste into .cursor/mcp.json in your project, or ~/.cursor/mcp.json

{
  "mcpServers": {
    "uniprof": {
      "command": "npx",
      "args": [
        "-y",
        "uniprof",
        "mcp",
        "run"
      ]
    }
  }
}

Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.

Open in VS Code

Or copy the settings yourself

Paste into .vscode/mcp.json in your project

{
  "servers": {
    "uniprof": {
      "type": "stdio",
      "command": "npx",
      "args": [
        "-y",
        "uniprof",
        "mcp",
        "run"
      ]
    }
  }
}

Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.

This one runs on your computer, and ChatGPT can only connect to servers on the internet. Pick another app.

Your choice stays on this device in a cookie; no account needed.

These settings are generated from the maker’s registry entry. If the README says otherwise, follow the README.

What it can do

Runs on your computer; the tool list appears in your AI app after you install it.

Basic checksi

We check information anyone can see; we have not read the code. Checks that do not apply are left out of the count. · Checked Nov 8, 2026

✓ Pass
Maker matches the repo
The registry name was verified with the GitHub account indragiek, which owns the repo
… Not checked yet
Still maintained
We have not read this repo from GitHub yet
… Not checked yet
Has a licence
We have not read this repo from GitHub yet
– Not applicable
Endpoint responds
This one runs on your computer; there is no internet endpoint
– Not applicable
Sign-in with OAuth
Runs on your computer and uses local settings instead
… Not checked yet
No hidden instructions in tool descriptions
Runs on your computer; we can only read tool lists from internet endpoints

Compared with others in Otheri

More stars than 0% of the other 18374 servers in this category
Published to the registry more recently than 0% of the other 18374 servers in this category
Basic checks equal to or better than 100% of the other 18374 servers in this category

Compared with every active server in this category in the registry. Stars count only when the publisher owns the repo.

Common questions

Is Uniprof safe?

It passed 1 of 1 applicable basic checks. We have not read the code. Before adding a key or token, see what access the server asks for and use a key with only the permissions you need.

What keys or codes does it need?

The registry does not list a required key, though some servers ask you to sign in on first use.

Does it run on my computer or in the cloud?

It runs on your computer. Your AI app starts it when needed, and data does not pass through the maker’s servers (unless it calls an online service itself).

Is it still maintained?

The GitHub repo last changed –, and the latest version (0.3.4) reached the registry 14 months ago.

Which apps can use it?

Apps that support MCP, such as Claude Desktop, Claude Code, Cursor and VS Code. You also need Node.js on your computer.

What can I use instead?

In the same category: Spec Driven Development, Remote Desktop Commander, Happo

Alternatives

Servers in the same category from other makers, sorted by stars.

Reading a card:★ GitHub starsiThree pillars: reviews · checks · usageiUpdated = latest version published to the registryi

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by formulahendry

MCP Server that facilitates spec-driven development workflows, not just Vibe Coding.

Runs on your computer★ 440MITUpdated 14 months ago

Install

Remote Desktop Commander

by desktop-commander

Remote Desktop Commander: use files, terminal and processes on your own computers from any AI app.

Runs in the cloud★ 211Updated 2 days ago

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Happo

by happo.io

Review Happo's visual and accessibility diffs: inspect comparisons, accept or reject, report flakes.

Runs in the cloudUpdated today

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README from the maker

Open the README

Copied from the maker’s repo as written. PopNx MCP has not reviewed or endorsed it. HTML, images and scripts are removed. Links lead off this site.

`uniprof`

uniprof simplifies CPU profiling for humans and AI agents. Profile any application without code changes or added dependencies.

# Profile and analyze any app in one step

npx uniprof python script.py

Supported Platforms
System Requirements
Installation
MCP Server
Quick Start
Host vs. Container Modes
Compiling with Debug Information
Documentation
Credits
License

Supported Platforms

uniprof implements a common interface over multiple profilers that specialize in different platforms and runtimes. It automatically detects which profiler to use based on the command being executed, runs the profiler, transforms the varying output formats into a single format, and runs statistical analysis on the data to identify hotspots.

Platform	Profiler	Container	Host	Min Version
Python	py-spy	✅	✅	Python 3.7+
Node.js	0x	✅	✅	Node 14+
Ruby	rbspy	✅	✅	Ruby 2.5+
PHP	Excimer	✅	✅	PHP 7.2+
JVM	async-profiler	✅	✅	Java 8+
.NET	dotnet-trace	✅	✅	.NET 5+
BEAM	perf	✅	✅*	OTP 24+
Native (macOS)	Instruments	❌	✅	Xcode 14.3+
Native (Linux)	perf	✅	✅	Linux 2.6.31+

\* Linux only for host mode

System Requirements

tl;dr: macOS or Linux with Docker installed

uniprof is designed to run on macOS and Linux but has primarily been developed & tested on macOS. If you find any bugs when running on Linux, please report them. Running on a Windows host is currenly not supported, but you can run on Linux installed on Windows via WSL2. Note that when using uniprof via WSL2, profiling native Windows executables is not supported, but you can still use the profilers for the higher level language runtimes like Python and Node.js.

Profiling tools are often non-trivial to set up correctly and can require elevated privileges. To simplify set up and provide better isolation for the profiled code, uniprof defaults to using Docker containers for each runtime that are pre-configured to run specific profiling tools. uniprof mounts your workspace in the container and executes the program with the profiler attached. uniprof also supports profiling outside the container if the host system has the profiling tools installed by running with the --mode host flag. The only case where containerized execution is not supported is when profiling a native Mach-O binary on macOS, since Apple Instruments cannot run inside a container.

Installation

npm install -g uniprof

MCP Server

# Install uniprof MCP server automatically

uniprof mcp install claudecode uniprof mcp install cursor uniprof mcp install vscode

Supported clients for auto-installation: amp, claudecode, codex, cursor, gemini, vscode, zed

If auto-installation is not supported for your client, add an MCP server using the stdio transport with the command npx -y uniprof mcp run.

For detailed MCP documentation see docs/mcp.md.

Quick Start

Profile and analyze in one step:

# Profile most languages and get immediate analysis

uniprof python app.py uniprof node server.js uniprof ruby script.rb uniprof java -jar myapp.jar uniprof dotnet MyApp.dll uniprof ./my-native-app

Profile and visualize flamegraph in browser

uniprof --visualize python app.py

Save profiles to analyze and visualize later:

# 1. Check environment (optional)

uniprof bootstrap

2. Record a profile

uniprof record -o profile.json -- python app.py uniprof record -o profile.json -- node server.js uniprof record -o profile.json -- ruby script.rb uniprof record -o profile.json -- php script.php uniprof record -o profile.json -- java -jar myapp.jar uniprof record -o profile.json -- ./gradlew run uniprof record -o profile.json -- ./mvnw spring-boot:run uniprof record -o profile.json -- dotnet MyApp.dll uniprof record -o profile.json -- elixir script.exs uniprof record -o profile.json -- mix run uniprof record -o profile.json -- ./my-native-app uniprof record -o profile.json -- /Applications/MyApp.app

3. Analyze profile data to find hotspots

uniprof analyze profile.json

4. Visualize flamegraph in the browser

uniprof visualize profile.json

For detailed command line options documentation see docs/cli.md.

Host vs. Container Modes

Use the --mode option to control how profiling runs:

`auto` (default): Prefer container mode when Docker is available; otherwise use host.
Language runtimes (Python/Node.js/Ruby/PHP/BEAM/JVM/.NET) default to container for zero-setup.
Native on macOS with Mach-O binaries uses host (Instruments). ELF binaries on macOS are supported in container mode.
Native on Linux defaults to container; host can be used if you prefer your local perf setup.
`host`: Force host-installed profilers.
`container`: Force Docker containers (not supported for macOS Instruments/Mach-O binaries).

# Auto mode (default)

uniprof record -o profile.json -- python script.py

Force host profilers

uniprof record --mode host -o profile.json -- python script.py

Force container mode

uniprof record --mode container -o profile.json -- ./my-linux-app

Compiling with Debug Information

Native profiling requires debug information for meaningful results. Debug symbols enable the profiler to map memory addresses to function names, providing readable output instead of just hexadecimal addresses. Additionally, frame pointers improve call stack accuracy, especially for optimized code.

Compiler	DWARF Debug Info	Frame Pointers	Notes
gcc	`gcc -g -o myapp main.c`	`gcc -fno-omit-frame-pointer -o myapp main.c`	Use `-g3` for maximum debug info, `-ggdb` for GDB-specific extensions
clang	`clang -g -o myapp main.cpp`	`clang -fno-omit-frame-pointer -o myapp main.cpp`	Use `-gfull` on macOS for complete debug info
swift	`swiftc -g main.swift`	`swiftc -Xcc -fno-omit-frame-pointer main.swift`	Debug builds include symbols by default with `swift build`

| cargo | cargo build (debug mode) cargo build --release + [profile.release] debug = true | RUSTFLAGS="-C force-frame-pointers=yes" cargo build | Debug builds include DWARF by default; for release builds, add debug = true to Cargo.toml | | go | go build -gcflags=all="-N -l" | Built-in, always enabled | Go includes debug info by default; -gcflags disables optimizations for better debugging | | zig | zig build-exe -O Debug main.zig | zig build-exe -fno-omit-frame-pointer main.zig | Use -O ReleaseSafe with debug info for optimized builds with symbols | | ghc | ghc -g -rtsopts main.hs | ghc -fno-omit-frame-pointer main.hs | Use -prof for profiling builds; -rtsopts enables runtime profiling options |

Documentation

[CLI Reference](docs/cli.md)
[Advanced Usage](docs/advanced.md)
[Platform Guides](docs/platforms/)
[MCP Server](docs/mcp.md)

Credits

py-spy (MIT) is used for Python profiling. Included in the uniprof-python image.
0x (MIT) is used for Node.js profiling. Included in the uniprof-nodejs image.
rbspy (MIT) is used for Ruby profiling. Included in the uniprof-ruby image.
Excimer (Apache 2.0) is used for PHP profiling. Included in the uniprof-php image.
async-profiler (Apache 2.0) is used for JVM profiling. Included in the uniprof-jvm image.
dotnet-trace (MIT) is used for .NET profiling. Included in the uniprof-dotnet image.
perf (GPL) is used for native profiling on Linux. Included in the uniprof-native and uniprof-beam images.
speedscope (MIT) is bundled with uniprof and used for flamegraph visualization

License

All uniprof source code in this repository is licensed under the MIT License. See LICENSE.

uniprof downloads and runs Docker images that bundle third-party software. The Dockerfiles used to build these images can be found in containers/. These images and their contents are not covered by the MIT license and are governed by their own licenses. uniprof does not grant rights to those components and does not relicense them.

Uniprof

Overview

PopNx MCP’s three pillars

What you can do with it

Install

Before you install

What it can do

Basic checksi

Maker matches the repo

Still maintained

Has a licence

Endpoint responds

Sign-in with OAuth

No hidden instructions in tool descriptions

Compared with others in Otheri

Common questions

Alternatives

Spec Driven Development

Remote Desktop Commander

Happo

README from the maker

uniprof

Table of Contents

Supported Platforms

System Requirements

Installation

MCP Server

Quick Start

Profile and visualize flamegraph in browser

2. Record a profile

3. Analyze profile data to find hotspots

4. Visualize flamegraph in the browser

Host vs. Container Modes

Force host profilers

Force container mode

Compiling with Debug Information

Documentation

Credits

License

`uniprof`