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PopNx MCPMCP servers for people who use AI · explained in plain words

Task tracking (sessions, ETA, dashboard)

by amineutronversion 0.3.1i★ –iRuns on your computer
Registry nameio.github.amineutron/mcp-tracking

Install

Overview

Description from the maker (original English)

Track long-running tasks in real time: sessions, steps, speed, ETA, HTTP API and TUI dashboard.

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: Docs, sheets & office work

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).
  • You fill in yourself: TRACKING_STATE_DIR, TRACKING_TOKEN_FILE

You need

  • PyPIPython’s uv installed (for the uvx command)

Which app do you use?

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": {
    "tracking": {
      "command": "uvx",
      "args": [
        "mcp-tracking"
      ],
      "env": {
        "TRACKING_STATE_DIR": "your-value",
        "TRACKING_TOKEN_FILE": "your-value"
      }
    }
  }
}

Run this in your terminal

claude mcp add tracking --env TRACKING_STATE_DIR=your-value --env TRACKING_TOKEN_FILE=your-value -- uvx mcp-tracking

Fill in the empty values before running it.

Open in Cursor

Or copy the settings yourself

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

{
  "mcpServers": {
    "tracking": {
      "command": "uvx",
      "args": [
        "mcp-tracking"
      ],
      "env": {
        "TRACKING_STATE_DIR": "your-value",
        "TRACKING_TOKEN_FILE": "your-value"
      }
    }
  }
}

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": {
    "tracking": {
      "type": "stdio",
      "command": "uvx",
      "args": [
        "mcp-tracking"
      ],
      "env": {
        "TRACKING_STATE_DIR": "your-value",
        "TRACKING_TOKEN_FILE": "your-value"
      }
    }
  }
}

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.

Values you fill in

  • TRACKING_STATE_DIR env
    Directory of the shared state file (default: XDG state dir)
  • TRACKING_TOKEN_FILE env
    File holding the local token required by the HTTP API

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 amineutron, 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 Docs, sheets & office worki

  • More stars than 0% of the other 2188 servers in this category
  • Published to the registry more recently than 90% of the other 2188 servers in this category
  • Basic checks equal to or better than 100% of the other 2188 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 Task tracking (sessions, ETA, dashboard) 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 (0.3.1) reached the registry 2 days ago.

Which apps can use it?

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

What can I use instead?

In the same category: Agent-Native Calendar, Excel MCP Server, MCP Server for Excel

Alternatives

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

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Excel automation for AI - Sheets, Power Query, DAX, VBA, Tables, Ranges and more. Windows only.

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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.

MCP Tracking

English summary. Local-first tracking of long-running tasks: an MCP server for Claude or Lyra, a small HTTP API on 127.0.0.1:8765 and a Textual terminal dashboard. Sessions have items, progress, logs and templates (download, machine, free, lyra_task, movie); pollers feed qBittorrent and Bazarr sessions automatically. Install: pip install . then mcp-tracking, mcp-tracking-api, mcp-tracking-ui. No cloud, no telemetry.

Serveur MCP de suivi en temps reel avec dashboard terminal. Permet a Claude/Lyra de tracker n'importe quelle operation longue ET alimente automatiquement les sessions depuis le media-server (qBittorrent, Bazarr, conversion DV).


Pourquoi c'est conçu pour des agents LLM

Un agent LLM (grand modèle de langage) qui lance une tâche longue (conversion, clone de VM, téléchargement) ne peut pas rester bloqué à la regarder, et il ne voit rien de ce qui se passe entre deux de ses tours. Ce serveur lui sert de mémoire de travail partagée :

  • Un bus de progression commun. L'agent, les scripts shell et les pollers écrivent dans les mêmes sessions (outils MCP ou API HTTP locale sur 127.0.0.1:8765) : l'agent lance la tâche, rend la main, et relit l'état plus tard avec tracking_get.
  • Des métriques calculées, pas devinées. Vitesse lissée sur 2 minutes, ETA, temps écoulé (metrics.py) : l'agent reçoit des chiffres au lieu d'extrapoler lui-même.
  • Une tâche muette se voit. Une session running sans mise à jour depuis 10 minutes est marquée stale : l'agent distingue « encore en cours » de « processus mort ».
  • Arrêter vise le bon processus. Une session peut porter le pid de son processus ; l'heure de démarrage relevée dans /proc évite de signaler un pid réutilisé par un autre programme. tracking_stop envoie SIGTERM et garde la session, tracking_kill envoie SIGKILL et la supprime.
  • Pas de mise à jour perdue. L'état est un fichier JSON local, protégé par un verrou flock et réécrit de façon atomique (storage.py) : plusieurs agents et scripts peuvent écrire en même temps.
  • Des gabarits par type de tâche (download, machine, movie, lyra_task, free) pour que chaque agent décrive son travail de la même façon.

Local-first et self-hosted : aucun cloud, aucune télémétrie, tout reste sur la machine (on-prem). Utilisé par Lyra, l'assistant vocal French-first, et par n'importe quel client MCP (Claude Code, Claude Desktop...).

Demo

Enregistree avec docs/demo/record.sh : server.py --test alimente quatre sessions simulees dans un repertoire d'etat temporaire (TRACKING_STATE_DIR), puis server.py --ui ouvre le dashboard dessus. Les sessions reelles ne sont pas touchees.

Sommaire

  • Pourquoi c'est conçu pour des agents LLM
  • Architecture
  • Installation
  • Services systemd
  • Lancement
  • Dashboard
  • Integration media-server
  • Outils MCP
  • Templates
  • Securite
  • Ajouter un template

Architecture

MCP/tracking/

server.py -- Serveur MCP (outils Claude/Lyra) + point d'entree --ui / --test api.py -- API HTTP locale (127.0.0.1:8765) pour les scripts externes mutations.py -- Mutations d'une session, partagees par api.py ET server.py (horodatage items, historique, niveaux de log, auto-completion) metrics.py -- Metriques derivees (vitesse, ETA, ecoule, stale) -- logique pure, calculees a la lecture, jamais stockees storage.py -- Persistence JSON atomique + verrou fichier + cache mtime + purge TTL models.py -- Modeles pydantic (TrackingSession, TrackingItem, LogEntry, ProgressPoint) templates.py -- Templates builtin + templates utilisateur (JSON) ui.py -- Dashboard Textual (TUI temps reel) + modales stop/kill sim.py -- Simulations de demo (server.py --test) poller.py -- Daemon polling qBittorrent (10s) + Bazarr (60s) tracking-api.service -- Unite systemd (systeme) pour api.py tracking-poller.service -- Unite systemd (systeme) pour poller.py install.sh / deploy.sh -- Installation initiale / redeploiement des services Makefile -- make test | smoke | deploy | ui tests/ -- unitaires (storage, metrics) + integration/ (API HTTP reelle)

Fichiers d'etat et configuration
FichierEmplacementSurcharge
tracking_state.json~/.local/state/tracking/TRACKING_STATE_DIR
poller_state.json~/.local/state/tracking/TRACKING_STATE_DIR
templates.json (templates utilisateur, optionnel)~/.config/tracking/TRACKING_TEMPLATES_FILE
credentials/*.cred (qBittorrent, Bazarr)a cote du code, gitignore--

Un ancien tracking_state.json a cote du code est migre automatiquement au premier demarrage (copie, jamais supprime).

Variables d'environnement de retention :

VariableDefautRole
TRACKING_TTL_DAYS7Purge des sessions done / error / paused
TRACKING_TTL_RUNNING_H24Purge des sessions running orphelines (plus mises a jour)
Flux de donnees complet
Claude/Lyra (outils MCP)

| v server.py ─────────────────────────────────────────┐ | qBittorrent API (poll 10s) | | | Bazarr API (poll 60s) ──> poller.py ──> api.py ──> mutations.py ──> storage.py ──> ~/.local/state/tracking/tracking_state.json | | | dv_webhook_server.py | v | | ui.py v | (rafraichit chaque seconde) dv_convert.py ──────────────────────────────────────> (metriques temps reel ffmpeg/dovi_tool)

Le fichier d'etat est ecrit a chaque modification via ecriture atomique (os.replace) sous verrou fichier (tracking_state.lock). Tous les processus (MCP, API, poller, dashboard) partagent cet unique fichier ; chaque lecture verifie le mtime pour invalider son cache.

Toute mutation (HTTP ou MCP) passe par mutations.py, qui garantit le meme comportement sur les deux chemins : started_at / finished_at poses sur les items et la session, historique de progression (fenetre glissante de 40 points), niveaux de log info / warn / error, auto-completion quand tous les items sont termines.

Metriques derivees

GET /sessions et tracking_get renvoient un bloc metrics calcule a la volee par metrics.py :

ChampSens
percentprogression (plafonnee a 100)
rate, rate_strvitesse sur les 120 dernieres secondes (2.0 MB/s, 30.0 u/min)
eta_seconds, eta_strtemps restant estime (session running uniquement)
elapsed_seconds, elapsed_strdepuis created_at jusqu'a finished_at ou maintenant
idle_seconds, stalestale = running sans mise a jour depuis 10 min (affiche dans le TUI)

Installation en une ligne

uvx mcp-tracking          # serveur MCP (stdio) ; avant publication : uvx --from git+https://github.com/amineutron/mcp-tracking mcp-tracking

uvx --from mcp-tracking mcp-tracking-api # API HTTP 127.0.0.1:8765 uvx --from mcp-tracking mcp-tracking-ui # tableau de bord terminal

Configuration Claude Desktop / Claude Code (mcpServers) :

{ "tracking": { "command": "uvx", "args": ["mcp-tracking"] } }

Installation

cd <dossier du dépôt>

Creer le venv et installer les dependances

uv venv .venv uv pip install "mcp[cli]>=1.0.0" "pydantic>=2.0" "textual>=0.80.0" "fastapi"

Le MCP est enregistre dans Claude Code (scope user) :

claude mcp list        # -> tracking: Connected

Pour reenregistrer :

claude mcp add tracking -s user -- \

/.venv/bin/python \ /server.py


Services systemd

Deux services tournent en permanence et se lancent au boot :

ServiceRolePort
tracking-api.serviceAPI HTTP locale pour scripts externes127.0.0.1:8765
tracking-poller.servicePoll qBittorrent (10s) + Bazarr (60s)--
Installation initiale et redeploiement
cd <dossier du dépôt>

./install.sh # premiere fois : venv + services (demande sudo) sudo ./deploy.sh # apres chaque mise a jour du code : stop, unites, restart, verif make smoke # sante rapide

Les instances MCP server.py deja ouvertes par des sessions Claude Code ne sont pas redemarrees par deploy.sh : reconnecter tracking via /mcp dans ces sessions.

Commandes utiles
# Etat

systemctl status tracking-api.service tracking-poller.service

Logs en direct

journalctl -fu tracking-poller.service journalctl -fu tracking-api.service

Redemarrage

sudo systemctl restart tracking-api.service tracking-poller.service

Test API

curl http://127.0.0.1:8765/health curl http://127.0.0.1:8765/sessions


Lancement

Dashboard (raccourci wofi)

Cherche "MCP Tracking" dans wofi/launcher. Lance le dashboard dans Kitty.

Dashboard (terminal)
# Toutes les sessions

/.venv/bin/python \ /server.py --ui

Filtre direct au lancement

.venv/bin/python server.py --ui --filter download .venv/bin/python server.py --ui --filter movie .venv/bin/python server.py --ui --filter errors

Via outil MCP (depuis Claude/Lyra)
open_tracking_ui()                             # toutes les sessions

open_tracking_ui(filter_template="lyra_task") # vue Lyra uniquement open_tracking_ui(filter_template="errors") # erreurs uniquement

Mode test (demo)
.venv/bin/python server.py --test

Simule 4 sessions en parallele : download, machine (12 noeuds), free, movie (pipeline DV complet).


Dashboard

Layout d'une session
[TEMPLATE]  Nom de la session  id:xxxxxxxx  (status)

[=============> ] 54.2% 27100 MB / 50000 MB champ_extra1: valeur | champ_extra2: valeur

[ok] item-1 100.0 GB -- termine [>] item-2 frame: 94231 / 172800 (54.5%) speed: 3.2x [ ] item-3 -- [!] item-4 erreur detail

Logs Erreurs 14:32:01 Message log 1 [!] item-4 14:32:04 Message log 2 14:32:08 ECHEC: details 14:32:07 Message log 3 --



Icones items
IconeStatutCouleur
[ ]pendinggris
[>]runningcyan
[ok]donevert
[!]errorrouge
Couleurs de session
CouleurStatut
cyanrunning
vertdone
rougeerror

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