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Stallhunt

by guillemversion 0.5.1i★ –iRuns on your computer
Registry nameio.github.guillem/stallhunt

Install

Overview

Description from the maker (original English)

Evidence-backed Linux performance triage over MCP

PopNx MCP’s three pillars

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

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.

Which app do you use?

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

Download the maker’s .mcpb file

The link goes to the maker’s own file; we do not host or repackage it. Open the file and Claude Desktop installs it.

Run this in your terminal

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.

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 7, 2026

  • ✓ Pass

    Maker matches the repo

    The registry name was verified with the GitHub account guillem, 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 32708 servers in this category
  • Published to the registry more recently than 30% of the other 32708 servers in this category
  • Basic checks equal to or better than 100% of the other 32708 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 Stallhunt 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 (0.5.1) reached the registry 2 months ago.

Which apps can use it?

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

What can I use instead?

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

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

Spec Driven Development

by formulahendry

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

Runs on your computer★ 440MITUpdated 14 months ago
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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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OpenTakeoff

by Kentucky-ai

Construction takeoff for AI agents: load plans, set scale, measure, count, export with provenance.

Runs on your computerUpdated 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.

Stallhunt

Stallhunt is a Linux-first command-line performance triage tool.

Repository: https://github.com/guillem/stallhunt

Traditional tools such as top, htop, iotop, vmstat, and iostat expose measurements. They are excellent tools, but the human operator still has to answer the harder question:

What is actually constraining useful work right now, who is suffering, and who is probably responsible?

Stallhunt aims to automate that reasoning.

Install

Requirements:

  • Linux 4.20 or newer with procfs mounted; readable PSI files under /proc/pressure are required for pressure verdicts,
  • Rust 1.85 or newer for source builds.

See docs/install.md for cargo install, release tarballs, and the support matrix.

From a clone:

cargo install --path .

stallhunt

Bare stallhunt runs a default 10-second hunt. On a terminal it renders a compact, color-coded report; piped or redirected output (stallhunt | cat, >file, CI) is unchanged plain text. Use --json for the full structured evidence, --verbose to expand the compact report's collapsed caveats back to full text, or --no-color (also NO_COLOR=1) to disable color without changing the layout:

stallhunt --json

stallhunt hunt --duration 30s stallhunt hunt --verbose

Capture and replay a normalized observation:

stallhunt record --duration 10s --output incident.json

stallhunt replay incident.json stallhunt redact incident.json --output incident.redacted.json

Follow finding lifecycle for a bounded number of rolling windows. On a terminal this opens a full-screen TUI (q quit, arrows/jk select, Enter/Space show or hide detail, PageUp/PageDown/Home/End scroll, h/? help). At 120×30 or larger it also shows the selected host/cgroup's six process-role lists beside the lifecycle panels; piped output or --json remain append-only text/JSON and carry the same scoped roles:

stallhunt watch --interval 2s --count 3

Serve Model Context Protocol tools over stdio so coding agents can query diagnoses directly — a resident sampler keeps a rolling view of recent pressure for instant answers (for Claude Code: claude mcp add stallhunt -- stallhunt mcp; see docs/mcp-server.md):

stallhunt mcp [--interval 2s] [--no-sampler]

Source packaging for an OpenAI local plugin, a Linux MCPB extension for compatible clients, and official MCP Registry metadata lives in the repository. These packages deliberately keep diagnosis local instead of hosting Stallhunt on a machine other than the one being investigated. See docs/directory-distribution.md.

Generate shell completions:

stallhunt completions bash > ~/.local/share/bash-completion/completions/stallhunt

stallhunt completions zsh > ~/.local/share/zsh/site-functions/_stallhunt

Recording output paths are not overwritten unless --force is supplied. Ten-second hunts are the normal diagnostic path; sub-second observations are telemetry smoke tests and do not receive healthy or pressure verdicts. See docs/development.md for validation and opt-in acceptance commands.

Core idea

The primary abstraction is lost time, not utilization.

High utilization is not automatically a problem. A machine using 95% of its RAM may be perfectly healthy. A CPU at 70% utilization may still have latency-sensitive work suffering from scheduler contention. The project therefore focuses on evidence of stalled progress:

  • CPU scheduler pressure,
  • I/O stalls,
  • memory pressure/reclaim,
  • lock contention,
  • network-related waits,
  • eventually deeper blocking chains.

Example output shape:

$ stallhunt

SYSTEM HEALTH: DEGRADED

  1. CPU scheduling contention SEVERE Impact: 23.4% pressure during observation Victims: postgres [4812], nginx [5120] Suspects: rustc [9231], ffmpeg [9401] Confidence: high

Evidence: CPU PSI some avg10: 23.4% run queue latency estimate: elevated rustc CPU consumption: 735% postgres runnable delay: 3.8s / 10s

  1. Block I/O contention MODERATE Device: nvme0n1 Victim: postgres [4812] Suspect: restic [7712] Confidence: medium

Memory: no significant pressure detected. High memory occupancy alone is not treated as a bottleneck.

This output is aspirational; the project will reach it incrementally.

Product principles

  1. Diagnose, do not merely display.
  2. Measure stalled progress whenever possible.
  3. Separate observation from inference.
  4. Show evidence for every diagnosis.
  5. Express uncertainty explicitly.
  6. Remain useful without eBPF.
  7. Stay cheap enough to run on a stressed system.
  8. Treat Git as the complete project memory.

Initial scope

The first useful release targets Linux and focuses on:

  • CPU scheduling contention,
  • memory pressure,
  • block I/O pressure,
  • per-process attribution where Linux exposes enough evidence,
  • cgroup/systemd-aware grouping when practical,
  • human-readable terminal output,
  • versioned machine-readable JSON (the pre-1.0 shape may evolve),
  • bounded observation windows,
  • deterministic offline fixture/replay analysis.

Later releases may add:

  • eBPF-based off-CPU analysis,
  • futex/lock contention,
  • syscall/blocking attribution,
  • network queue/socket diagnosis,
  • dependency/wait graphs,
  • richer cgroup/container analysis.

Repository map

.

├── AGENTS.md ├── CHANGELOG.md ├── Cargo.toml ├── LICENSE-APACHE ├── LICENSE-MIT ├── README.md ├── src/ │ ├── analysis.rs │ ├── cgroup.rs │ ├── cli.rs │ ├── cpu.rs │ ├── duration_us.rs │ ├── io.rs │ ├── main.rs │ ├── mcp/ │ ├── memory.rs │ ├── observe.rs │ ├── psi.rs │ ├── record.rs │ ├── render.rs │ ├── report.rs │ ├── style.rs │ ├── tui/ │ └── watch.rs ├── tests/ │ ├── cgroup_acceptance.rs │ ├── cli.rs │ ├── cpu_acceptance.rs │ ├── io_acceptance.rs │ ├── mcp.rs │ ├── memory_acceptance.rs │ └── fixtures/ │ ├── cpu/ │ └── proc-* └── docs/ ├── README.md ├── install.md ├── product.md ├── architecture.md ├── data-model.md ├── telemetry.md ├── inference-engine.md ├── cli-ux.md ├── security-privileges.md ├── testing.md ├── development.md ├── codex-workflow.md ├── experiments.md ├── references.md ├── roadmap.md ├── status.md ├── glossary.md └── decisions/

Start with AGENTS.md, then docs/README.md.

Privacy Policy

Stallhunt reads bounded local Linux telemetry and does not independently send it over the network. MCP clients may transmit tool results according to their own data policies. See the full Stallhunt privacy policy for the data, storage, sharing, retention, and contact disclosures.

License

Dual-licensed under MIT or Apache-2.0, at your option.

Current state

For the current milestone, implemented capabilities, validation, known limits, and the next recommended task, see docs/status.md. Planned sequencing remains in docs/roadmap.md.

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