Installation
Ajoutez ceci à la configuration MCP de votre client (Claude Desktop : claude_desktop_config.json ; Cursor : .cursor/mcp.json ; VS Code et les autres utilisent la même forme « mcpServers »).
https://github.com/guillem/stallhunt/releases/download/v0.5.1/stallhunt-0.5.1-x86_64-unknown-linux-gnu.mcpbDocumentation
Tirée du README du projet sur GitHub, actualisée chaque semaine. Les liens ouvrent GitHub.
Sommaire
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/pressureare 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
2. 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
- Diagnose, do not merely display.
- Measure stalled progress whenever possible.
- Separate observation from inference.
- Show evidence for every diagnosis.
- Express uncertainty explicitly.
- Remain useful without eBPF.
- Stay cheap enough to run on a stressed system.
- 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.
Questions fréquentes
Qu'est-ce que Stallhunt ?
Evidence-backed Linux performance triage over MCP
Comment installer Stallhunt ?
Suivez les étapes d'installation de sa documentation, affichée sur cette page et dans son dépôt.
Faut-il une clé API pour Stallhunt ?
Son entrée au registre n'indique aucune clé ni secret obligatoire. Certaines fonctions peuvent tout de même demander un compte auprès du service auquel il se connecte.
Stallhunt est-il open source ?
Oui. Son code est sur GitHub sous licence Apache-2.0.
Qui maintient Stallhunt ?
Il est publié par guillem. AI.tn le référence sans le maintenir ni le recommander.
Issu du registre MCP officiel. Être listé n'est pas une recommandation : vérifiez le code et les permissions d'un serveur avant de lui donner accès à vos données.