looker-studio-mcp
Automates Google Looker Studio (formerly Data Studio) for creating, editing, and managing dashboards, including adding data sources (Google Sheets, BigQuery) and charts (Scorecard, Table, Time Series), taking screenshots, and exporting data.
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@looker-studio-mcptake a screenshot of my monthly revenue dashboard"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
đŻ Looker Studio MCP (Model Context Protocol)
Automatisation de Looker Studio via Playwright â crĂ©ation, modification, et gestion de dashboards.
đ FonctionnalitĂ©s
â Login Google automatisĂ© avec profil persistant
â CrĂ©ation de rapports Looker Studio
â Ajout de sources (Google Sheets, BigQuery, etc.)
â Ajout de graphiques (Scorecard, Table, Time Series, etc.)
â Screenshots de dashboards
â Export CSV des donnĂ©es
â Listage des rapports existants
â Mode interactif avec CLI
Related MCP server: Playwright MCP Automation
đŠ Installation
# Cloner le repo
git clone https://github.com/ton-user/looker-studio-mcp.git
cd looker-studio-mcp
# Installer les dépendances
pip install -r requirements.txt
# Installer Chromium pour Playwright
playwright install chromiumđŻ Utilisation
Mode rapide (screenshot)
python3 looker-studio-automation.py screenshot "https://lookerstudio.google.com/report/xxx" /tmp/dashboard.pngMode interactif
python3 looker-studio-automation.py interactiveCommandes disponibles :
Commande | Description |
| Créer un nouveau rapport |
| Lister mes rapports |
| Capture d'écran |
| Ajouter un graphique |
| Exporter en CSV |
| Ouvrir une URL |
| Quitter |
En tant que module Python
import asyncio
from looker_studio_automation import LookerStudioAutomation
async def main():
auto = LookerStudioAutomation(headless=False)
await auto.start()
# Ouvrir Looker Studio
await auto.open_looker_studio()
# Attendre le login manuel
input("Connecté? Appuie sur Entree...")
# Créer un rapport
await auto.create_report("Mon Dashboard")
# Ajouter un graphique
await auto.add_chart("Scorecard")
# Screenshot
await auto.take_screenshot("/tmp/dashboard.png")
await auto.stop()
asyncio.run(main())đ Structure du projet
looker-studio-mcp/
âââ looker-studio-automation.py # Script principal
âââ requirements.txt # DĂ©pendances Python
âââ README.md # Documentation
âââ LICENSE # License MIT
âââ .gitignore # Fichiers exclus
âââ examples/ # Exemples d'utilisation
âââ example_usage.pyđ§ DĂ©pendances
playwright>=1.40.0â ïž Pitfalls & Notes
Profil Chrome persistant
Le script utilise un profil Chrome persistant (~/.hermes/looker-studio-chrome/) pour garder la session Google entre les exécutions. Cela évite de se reconnecter à chaque fois.
Login Google
La premiÚre exécution nécessite un login manuel dans le navigateur. Ensuite, la session est sauvegardée.
Sélecteurs CSS
Google modifie rĂ©guliĂšrement les sĂ©lecteurs CSS de Looker Studio. Si le script ne fonctionne plus, il faudra peut-ĂȘtre mettre Ă jour les sĂ©lecteurs.
Headless mode
Certains éléments UI ne fonctionnent pas en mode headless. Utilisez headless=False pour les opérations complexes.
đ€ Contribuer
Fork le projet
Créer une branche (
git checkout -b feature/amazing-feature)Commit (
git commit -m 'Add amazing feature')Push (
git push origin feature/amazing-feature)Ouvrir une Pull Request
đ License
Ce projet est sous License MIT â voir le fichier LICENSE pour plus de dĂ©tails.
đ CrĂ©dits
Playwright â Automatisation de navigateur
Google Looker Studio â Outil de visualisation de donnĂ©es
đ§ Contact
Jmarc â jmd@talandria.fr
Projet Link: https://github.com/ton-user/looker-studio-mcp
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