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MEDAS MCP

by bluedragonDC

MEDAS MCP (noch in Entwicklung!)

MCP-Server für TÜİK MEDAS (Indikatoren des türkischen Statistikinstituts).

Funktionen

  • 92 Themen — alle statistischen Kategorien von TÜİK

  • 400+ Indikatoren — sofort gecacht, Live-Fallback verfügbar

  • Dynamische Erkennung — 0 fest verdrahtete Widget-IDs, passt sich UI-Änderungen an

  • Cache-first — list_topics/get_indicators/download alle <50ms aus dem Cache

  • Binäres XLS — xlwt-CDFV2-Excel-Ausgabe (gleiches Format wie MEDAS pivot.xls)

  • Intelligentes Cascading — wählt automatisch Pflicht-Aufschlüsselungen aus (COICOP, SITC, etc.)

  • ZK-Widget-API — robuste kırılım-Behandlung über zk.Widget.$().fire()

Related MCP server: turkiye-veri-mcp

Installation

pip install playwright xlwt httpx
playwright install chromium

Oder mit uv:

uv pip install -e .
playwright install chromium

Verwendung

Als MCP-Server (stdio)

python server.py

Pi-Integration

Hinzufügen zu ~/.pi/config.json:

{
  "extensions": {
    "medas": {
      "command": "python",
      "args": ["/path/to/medas_mcp/server.py"],
      "cwd": "/path/to/medas_mcp"
    }
  }
}

Claude Desktop

{
  "mcpServers": {
    "medas": {
      "command": "python",
      "args": ["/path/to/medas_mcp/server.py"]
    }
  }
}

Tools

Tool

Beschreibung

Geschwindigkeit

list_topics(search?)

Listet 92 TÜİK-Themen

<50ms (Cache)

get_indicators(topic_index)

Indikatoren und Cascading-Zweige abrufen

<50ms (Cache)

download(topic_index, indicators?, format?, save_path?, live?)

XLS/CSV-Bericht herunterladen

<50ms Cache / ~12s live

Beispielablauf

1. list_topics("fiyat") → [{index:78, label:"Tüketici Fiyat Endeksi"}]
2. get_indicators(78) → {count:12, indicators:[...]}
3. download(78, format="xls") → /tmp/MEDAS_Tüketici_Fiyat_Endeksi_20260820.xls

Architektur

AI Agent  ⇄  MCP (stdio)  ⇄  server.py  ⇄  medas_client.py
                                            ├─ cache (data/*.json) → instant
                                            └─ Playwright (live=true) → ZK AU protocol

Cache vs. Live

Modus

Quelle

Geschwindigkeit

Daten

live=false (Standard)

data/*.json-Cache

<50ms

Indikatornamen + Mock-Werte

live=true

POST /medas/zkau → GET /pivot.xls

~12s

Echte MEDAS-Pivot-Tabelle

Dateien

medas_mcp/
├── server.py              # MCP server (3 tools)
├── medas_client.py        # Hybrid client (cache + live Playwright)
├── KNOWHOW.md             # ZK AU protocol traffic notes
├── AGENTS.md              # AI agent instructions
├── README.md              # This file
├── pyproject.toml         # Package metadata
├── .gitignore
└── data/
    ├── topic_mapping.json  # 92 topics with URLs
    ├── topic_gosterge.json # Indicators + cascading branches
    └── medas_unified.json  # Unified dataset

ZK-AU-Protokoll

  • POST /medas/zkau;jsessionid=XXX mit dtid + gebündelten cmd_n=onSelect/onClick

  • Widget-IDs ändern sich in jeder Sitzung — werden dynamisch über das DOM erkannt

  • Cascading: zk.Widget.$('#selectId').fire('onSelect', {items:[itemId], reference:itemId})

Lizenz

MIT

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