data-marketplace-mcp-server
Enables discovery, governance, access, and publishing of data products in an internal data marketplace, with integration for Cloudera AI Workbench and Apache Atlas.
README
Internal Data Marketplace MCP Server
MCP-Agent für einen internen Data Marketplace (Data Mesh Store / Internal Data Portal) — optimiert für Cloudera AI Workbench.
Der Fokus liegt auf Kollaboration, Governance und Systemintegration: Datensilos aufbrechen, damit Teams Daten self-service finden, prüfen und freigeben lassen können — ohne monatelange IT-Tickets.
Basisfunktionen
| Bereich | Funktion | MCP-Tool |
|---|---|---|
| Discovery | Unternehmensweiter Datenkatalog | search_data_products |
| Discovery | Metadaten & Ownership | get_data_product |
| UX | Vorschau & Profiling | preview_data_product |
| Governance | Business Glossary | search_glossary |
| Governance | Data Lineage | get_product_lineage |
| Access | Zugriff anfordern („Warenkorb“) | request_data_access |
| Access | Anfragen verwalten | list_access_requests |
| Access | Genehmigen / Ablehnen | approve_access_request, reject_access_request |
| Publishing | Datenprodukt anbieten | publish_data_product |
| Trust | Zertifizierungs-Badge | certify_data_product |
| Feedback | Sterne-Bewertung | submit_product_feedback |
Demo-Szenario (Sarah & Thomas)
- Suche:
search_data_products(query="Kundenhistorie Kündigungen", region="DACH", certified_only=true) - Prüfung:
get_data_product("DP-SALES-CHURN-HIST"),preview_data_product(...),get_product_lineage(...) - Zugriff:
request_data_access(...)mit Nutzungszweck und Zielumgebung - Genehmigung: Thomas ruft
approve_access_request(...)auf → automatisches Provisioning - Feedback:
submit_product_feedback(...)nach Nutzung
Lokale Entwicklung
cd data-marketplace-mcp-server
uv sync
uv run python -m pytest tests/ -q
uv run run-marketplace # stdio MCP (Cursor / Claude Desktop)
Cloudera AI Workbench Deployment
Option A: Docker Application
-
Build & push image:
docker build -t data-marketplace-mcp:0.1.0 . -
In Cloudera AI Workbench eine neue Application anlegen:
- Runtime: Docker
- Port:
8080 - Env: siehe
cai-workbench/app.yaml
-
Persistent volume auf
/datamounten (MARKETPLACE_DATA_DIR).
Option B: HTTP MCP für Workbench Agents
export MCP_TRANSPORT=http
export MCP_HOST=0.0.0.0
export MCP_PORT=8080
export MARKETPLACE_DATA_DIR=/data
run-marketplace
Workbench-Agents verbinden sich per MCP Streamable HTTP auf Port 8080.
Konfiguration
| Variable | Default | Beschreibung |
|---|---|---|
MCP_TRANSPORT |
stdio |
stdio oder http für Workbench |
MCP_HOST |
0.0.0.0 |
Bind-Adresse (HTTP) |
MCP_PORT |
8080 |
Port (HTTP) |
MARKETPLACE_DATA_DIR |
./data |
Persistenz für Produkte & Anfragen |
ATLAS_GATEWAY_URL |
— | Optional: Atlas-Katalog anreichern |
ATLAS_USER / ATLAS_PASS |
— | Knox/Atlas Auth |
MARKETPLACE_PROVISIONING_WEBHOOK |
— | Optional: Ranger/Entra-ID Automation |
Atlas-Integration (CDP)
Wenn ATLAS_* gesetzt ist:
search_data_productsliefert zusätzlich Atlas-Treffer (hive_table,iceberg_table)get_product_lineagenutzt Atlas Lineage APIsearch_glossarydurchsucht Atlas Business Glossary
Provisioning (Erweiterung)
Standardmäßig simuliert approve_access_request das Provisioning (Gruppenzuweisung, Zugriff freischalten).
Für echte Automation einen Webhook setzen:
export MARKETPLACE_PROVISIONING_WEBHOOK=https://your-provisioner/ranger-or-entra
Der Webhook erhält JSON mit request und product und kann Ranger Policies oder Entra-ID-Gruppen steuern.
MCP in Cursor konfigurieren
{
"mcpServers": {
"data-marketplace": {
"command": "uv",
"args": ["run", "--directory", "/path/to/data-marketplace-mcp-server", "run-marketplace"],
"env": {
"ATLAS_GATEWAY_URL": "https://<host>/<topology>/cdp-proxy-api/atlas/api/atlas/",
"ATLAS_USER": "<user>",
"ATLAS_PASS": "<pass>"
}
}
}
}
Lizenz
Apache-2.0
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