data-marketplace-mcp-server
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., "@data-marketplace-mcp-serversearch for certified data products about customer churn in DACH region"
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.
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 |
|
Discovery | Metadaten & Ownership |
|
UX | Vorschau & Profiling |
|
Governance | Business Glossary |
|
Governance | Data Lineage |
|
Access | Zugriff anfordern („Warenkorb“) |
|
Access | Anfragen verwalten |
|
Access | Genehmigen / Ablehnen |
|
Publishing | Datenprodukt anbieten |
|
Trust | Zertifizierungs-Badge |
|
Feedback | Sterne-Bewertung |
|
Related MCP server: Data Product MCP
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 ZielumgebungGenehmigung: Thomas ruft
approve_access_request(...)auf → automatisches ProvisioningFeedback:
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:
8080Env: 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-marketplaceWorkbench-Agents verbinden sich per MCP Streamable HTTP auf Port 8080.
Konfiguration
Variable | Default | Beschreibung |
|
|
|
|
| Bind-Adresse (HTTP) |
|
| Port (HTTP) |
|
| Persistenz für Produkte & Anfragen |
| — | Optional: Atlas-Katalog anreichern |
| — | Knox/Atlas Auth |
| — | 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-entraDer 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
Maintenance
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