How do I use KPI Monitoring MCP Server?

1. Click on "Install Server". 2. Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state. 3. In the chat, type @ followed by the MCP server name and your instructions, e.g., "@KPI Monitoring MCP Server Analyze online retail sales and create a dashboard with key KPIs" 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.

KPI Monitoring MCP Server

by Safae-az

Overview Schema Related Servers Score Discussions

Python

Local

🚀 KPI Monitoring System — Run Guide

Prérequis

Python 3.10+
pip

Installation

# 1. Cloner le repo
git clone <url-du-repo>
cd project

# 2. Installer les dépendances
pip install -r requirements.txt

# 3. Configurer la clé API
cp .env.example .env
# Éditer .env et mettre ta clé Anthropic :
# ANTHROPIC_API_KEY=sk-ant-xxxxxxx

Lancement

Terminal 1 — MCP Server (port 8000)

uvicorn app.mcp.server:app --port 8000 --reload

Terminal 2 — API principale (port 8001)

uvicorn app.main:app --port 8001 --reload

Tester le système

# Test 1 : MCP Server opérationnel ?
curl http://localhost:8000/health

# Test 2 : Lister les outils disponibles
curl http://localhost:8000/tools

# Test 3 : Vérifier les permissions d'un agent
curl http://localhost:8000/permissions/data_engineer

# Test 4 : Lancer un pipeline complet
curl -X POST http://localhost:8001/run/start \
  -F "file=@online_retail_raw.csv" \
  -F "objective=Analyse les KPIs de ventes et génère un dashboard"

# Test 5 : Voir les logs d'un run
curl http://localhost:8001/run/run_20241201_120000/logs

Structure des fichiers produits

runs/
  run_20241201_120000/
    metadata.json              ← info du run
    decisions.jsonl            ← décisions des agents
    tool_calls.jsonl           ← appels MCP
    artifacts/
      cleaned_data.csv         ← produit par Data Engineer
      insights.json            ← KPIs produits par Data Scientist
      report.html              ← rapport final produit par Reporter
      charts/
        ca_mensuel.html        ← graphiques produits par BI Agent
        top_pays.html
        repartition.html
      dashboard.html           ← dashboard assemblé par BI Agent

Responsabilités par personne

Fichier	Codé par
app/mcp/server.py	P1
app/orchestrator/engine.py	P1
app/agents/devops_agent.py	P1
app/storage/artifact_store.py	P1
app/tools/load_dataset.py	P2
app/tools/quality_check.py	P2
app/tools/clean_data.py	P2
app/tools/run_analysis.py	P2
app/agents/data_engineer.py	P2
app/agents/data_scientist.py	P2
app/tools/generate_chart.py	P3
app/tools/publish_dashboard.py	P3
app/agents/bi_agent.py	P3
app/tools/compile_report.py	P4
app/agents/reporter.py	P4
app/main.py (UI backend)	P4

test-pfa

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license - not found

quality - not tested

maintenance

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Resources

GitHub Repository

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