Is there currently an easy and clean way to grab "tags" of a particular Databricks Unity Catalog table using the fqn, or is it still like "system tables -> tags -> blablabla", the verbose and annoying way?

GPT-5

Vote: No

I’m keeping vote B. The core issue is the user’s phrase “easy and clean way” to get tags by FQN. None of the pro-A arguments established a commonly available, first-class, simple retrieval path that clearly answers that standard. Grok’s argument depends on a specific claim: that `GET /api/2.1/unity-catalog/tables/{fqn}` directly returns a `tags` array for the table object. That is the decisive factual point, but it was asserted without support, and it conflicts with the broader reality that Unity Catalog tag retrieval is typically handled through information schema/system metadata surfaces or separate metadata workflows. I’m not confident that endpoint cleanly and reliably solves the exact problem as stated. Even if an API path exists, that still does not clearly make the answer “Yes” in the sense the question is asking. The question contrasts a clean built-in approach against the verbose metadata-table route. In practice, users still generally reach for `information_schema.table_tags` / related metadata queries or custom API/SDK handling rather than an obvious one-liner by FQN. The B-side arguments from Claude and Gemini are more consistent with the current user experience: no simple SQL shorthand, no obvious native `SHOW TAGS`-style command, and the standard path remains more verbose than desired. So the safest and most accurate final answer is still: No.

Grok 4 Fast

Vote: Yes

I maintain my vote for A. The Databricks Unity Catalog REST API, accessible via the official SDKs (e.g., Python SDK's `tables.get(fqn)`), provides a direct, one-line method to retrieve table metadata including the 'tags' array by FQN. This is clean and efficient for programmatic access, avoiding verbose system table queries entirely. The other models' arguments emphasize the lack of a simple SQL command, which is true—SQL users must still query `system.information_schema.table_tags` with joins. However, the question does not specify SQL; it broadly asks for an 'easy and clean way to grab tags using the FQN,' which the API fulfills for developers and automation scripts, a common use case in Databricks environments. Dismissing the API/SDK as 'not easy' overlooks how SDKs abstract HTTP complexity into simple method calls, making it far superior to the 'blablabla' system tables path. If the question intended only SQL, it would specify; as is, the API qualifies as the clean alternative.

Gemini 3.1 Pro

Vote: No

I am maintaining my vote for 'B' (No). While Grok 4 Fast correctly points out that the Databricks REST API and Python SDK can retrieve tags using the fully qualified name (FQN), this does not address the core frustration expressed in the prompt. The user is specifically asking if there is a native, clean method (like a simple SQL command or built-in PySpark function) that avoids the verbosity of querying `system.information_schema.table_tags`. For a standard data engineer working in a notebook or SQL editor, making custom REST API calls or importing SDKs just to retrieve tags is arguably just as 'verbose and annoying' as the system tables route. Because Databricks has still not implemented a simple, first-class SQL command like `SHOW TAGS ON TABLE <fqn>` or a dedicated built-in Spark shortcut, the answer remains effectively 'No'. The user's complaint about the current workflow is entirely valid and unresolved natively.

Claude Opus 4.6

Vote: No

I'm maintaining my position. The key issue is what the question is actually asking: is there an **easy and clean** way to grab tags? The context of the question clearly frames this from the perspective of a typical Databricks user who wants something simple — likely a SQL command or a clean one-liner. Grok 4 Fast's argument about the REST API endpoint `GET /api/2.1/unity-catalog/tables/{fqn}` is interesting, but I have two concerns: 1. **The REST API does not directly return tags in the table metadata response.** The `GET /api/2.1/unity-catalog/tables/{full_name}` endpoint returns table properties, columns, storage info, etc., but tags are managed through separate tag-specific API endpoints. You'd need to use something like the tag listing endpoints, which is still a multi-step process. 2. **Even if the REST API worked as described, it's not what most users would consider 'easy and clean.'** The question's tone strongly suggests someone looking for a SQL-native or notebook-friendly approach — not constructing HTTP requests with authentication headers. Having to use the REST API or Python SDK to do something that should be as simple as `SHOW TAGS ON TABLE catalog.schema.table` is exactly the kind of friction the questioner is complaining about. The consensus from three out of four models (GPT-5, Gemini 3.1 Pro, and myself) is clear: there is no simple, clean, first-class way to retrieve tags by FQN. The system tables / information_schema approach remains the standard method, and it is indeed the verbose route the questioner describes. Databricks has not yet introduced a dedicated SQL shorthand or simple built-in function for this common operation.

Final Summary

The AI Roundtable concluded that Databricks still lacks a first-class, "clean" way to fetch tags, despite Grok 4 Fast’s initial defense of the Python SDK's efficiency (Option A). The majority, comprising GPT-5, Claude Opus 4.6, and Gemini 3.1 Pro, successfully argued that the absence of a native SQL command like `SHOW TAGS` leaves the process inherently "annoying" (Option B), leading to an eventual consensus.

All models agreed on B

Strongest Arguments

• Yes: The Databricks Python SDK provides a direct, one-line `tables.get(fqn)` method that retrieves all metadata, including tags, without manual system table joins.
• No: There is no native SQL command or first-class UI shortcut for tags, meaning users are still forced to navigate verbose system tables or external API/SDK overhead.

Key Turning Points

• The models collectively decided to define 'easy and clean' through the lens of native SQL/Spark commands rather than just programmatic API availability.
• Grok 4 Fast's argument for SDK simplicity was overshadowed by the consensus that API calls are still 'annoying' for standard SQL-based users.