:heavy_exclamation_mark: This project builds a read-only MCP server. For full read, write, update, delete, and action capabilities and a simplified
:heavy_exclamation_mark: This project builds a read-only MCP server. For full read, write, update, delete, and action capabilities and a simplified setup, check out. The azure data catalog by cdata mcp server wraps that behind the Model Context Protocol, so an assistant can use it through 3 defined tools rather than through you.
CData JDBC Driver connects to Azure Data Catalog by exposing them as relational SQL models.
Being a remote server, there is no local install. You register the endpoint with your client, authorise it once, and the tools appear.
Everything the assistant can do here goes through one of these:
source_get_tables — The source_get_tables tool exposed by this serversource_get_columns — The source_get_columns tool exposed by this serversource_run_query — The source_run_query tool exposed by this serverPlenty of cloud and infrastructure servers cover similar ground. The differences that matter in practice are scope of access and how much setup stands between you and a working tool call. Azure Data Catalog By Cdata's toolset — source_get_tables, source_get_columns, source_run_query — is a fair guide to whether it matches your workflow. It is maintained by CDataSoftware; worth a glance at recent repository activity before you build anything load-bearing on it.
SyncDev reviews every entry in this directory against the project's own documentation before publishing, and revisits them as servers change.
| Tool | What it does |
|---|---|
| source_get_tables | The source_get_tables tool exposed by this server. |
| source_get_columns | The source_get_columns tool exposed by this server. |
| source_run_query | The source_run_query tool exposed by this server. |
**Linux/Mac**
```json
{
"mcpServers": {
"{classname_dash}": {
"command": "/PATH/TO/java",
"args": [
"-jar",
"/PATH/TO/CDataMCP-jar-with-dependencies.jar",
"/PATH/TO/azure-data-catalog.prp"
]
},
...
}
}Configuration as documented by the project. Restart the client after saving.
Manage your whole Supabase project in conversation — database, auth, storage, Edge Functions and branches.
Stop letting your assistant hallucinate n8n node parameters — this server hands it the real schemas, templates and validation.
AWS Labs' official server suite — current AWS docs, CDK guidance, cost analysis and service tools.
Cloud browsers for AI agents — automation sessions that run in Browserbase's fleet, not on your machine.
Workers, KV, R2 and D1 by conversation — Cloudflare's official remote servers for building and observability.
Dashboards, Prometheus and Loki queries, incidents and alerts — observability by conversation.