A practical map of what teams do with the DataGalaxy MCP Server — organised under two pillars:
- using your metadata as trusted context inside any AI client, and
- enriching the catalog from anywhere, with or without AI.
The DataGalaxy MCP Server connects your governed catalog to any MCP-compatible AI client — a chat assistant, a Microsoft Teams app, an IDE, or a custom product. It exposes your glossary, documentation, lineage, ownership, and tags as structured context, so answers stay grounded in your approved definitions instead of guesses. The guiding idea: bring the catalog to your users, not your users to the catalog.
Three principles hold across every use case below:
- Metadata, not data — it exposes context (terms, documentation, lineage, ownership, tags), never the underlying data.
- Governance inherited — every request respects your existing roles, domains, and access controls, and is authenticated and auditable. A client only ever surfaces what the user is allowed to see.
- Client-agnostic and lightweight — it works with any MCP client; setup is a URL plus an auth token, and the server is hosted and maintained by DataGalaxy.
The use cases fall into two pillars:
- Pillar 1 – Using metadata: consuming trusted business context inside the clients your teams already use.
- Pillar 2 – Enriching the catalog: updating and improving the catalog from anywhere, with or without AI in the loop.
Pillar 1 — Using metadata: trusted context in every client
Users rarely formulate a perfect query — they ask approximate questions in natural language, and an object's metadata is what they are really after. Across the use cases below, the payoff is consistent: fewer tickets to the data team, less context switching, and answers that always follow your governance model.
1. Self-service discovery
Business users · analysts · leadership
Plain-language questions, answered from the catalog:
"Which table holds active customer subscriptions?"
"What does 'Net Revenue' mean, how is it calculated, and who owns it?" — definition, calculation rule, and owner in one answer.
How: semantic_search / natural_language_search → get_object_details
Value: the catalog meets people where they are; adoption rises because there is only one interface to learn.
2. Definitions in the flow of work (chat & Teams)
Business users
Employees look up definitions and object information without leaving the tool they live in all day.
Value: trusted context appears inside everyday tools, and every answer is permission-aware.
3. Context inside the IDE
Data engineers · scientists · developers
Inside an IDE-based assistant, validate a column against the catalog, or trace where a field comes from before refactoring.
How: get_linked_objects · get_ancestors · get_object_details
Value: no toggling between a documentation portal and the code editor — the definition sits next to the code.
4. Grounding downstream AI (reduce hallucination)
AI / app developers · AI teams
Use the server to ground other products. A customer-facing data chatbot anchors its answers in certified definitions before replying.
How: semantic_search · get_object_details
Value: trustworthy, governance-aligned answers inside the products your people and customers actually use.
5. Impact analysis
Stewards · analysts · data engineers
"If we change the Orders table schema, which dashboards and reports break?"
How: downstream traversal via get_linked_objects (multi-hop chains may need iterative lookups).
Value: faster, explainable impact assessment grounded in real lineage.
6. Onboarding & data literacy
New joiners · business users
"Explain the Customer 360 dataset and how it connects to the CRM."
How: get_object_details · get_linked_objects
Value: self-serve ramp-up that reduces reliance on tribal knowledge — several hours less to onboard a new user.
7. Compliance & classification audit
DPO · governance managers · auditors
"Scan the Insurance workspace: which objects look like they hold personal data but aren't tagged PII, and which PII objects have no owner?"
The assistant doesn't just retrieve what's already tagged — it reasons over the metadata. It reads object names, descriptions, and columns, compares them against your tag vocabulary, and flags likely mis-classifications and gaps: a column like client_email sitting in an untagged table, a "Confidential" object with no owner, sensitivity that doesn't match the content.
How: list_workspaces_and_versions · search_objects · get_object_details · get_object_tags · get_tags → create_comment (optional, to log a fix suggestion)
Value: turns an audit from manual sampling into an AI-driven risk scan that surfaces what's wrong, not just what exists — and every finding is permission-aware, auditable, and reviewable before any change is made.
8. Governance health check & consistency analysis
Stewards · governance managers
"Are the Customer domain's tags consistent? Flag any child object whose classification disagrees with its parent, and summarise the open discussions I should act on first."
The assistant traverses the hierarchy and linked objects, then compares tags and sensitivity for contradictions — a public child under a confidential parent, two linked objects classified differently, a definition that conflicts with an open comment. It also reads the discussion and task threads and triages them into a ranked action list.
How: get_ancestors · get_linked_objects · get_object_tags · get_comments · get_tasks → create_comment (optional)
Value: a reasoned health check, not a data dump — classification drift caught early, definitions kept coherent, and the human layer of the catalog prioritised for you.
Pillar 2 — Enriching the catalog from everywhere, with or without AI
The hardest problem in any catalog is the metadata that does not exist yet: business users can't understand undescribed objects, and manual description work is slow for stewards. The server lets enrichment happen wherever the conversation already is — across a full spectrum from manual to fully AI-assisted.
9. Description autogeneration — single & batch
Stewards · data engineers
Generate clear object descriptions instead of writing them by hand — one at a time, or for several objects at once. The model can be fed existing catalog metadata and internal company knowledge, so the result reflects how your organisation actually uses the object.
How: get_data_sources / search_objects → get_object_details → update_object
Value & reported outcomes: up to 4 hours/week saved for stewardship · ~1 day/month saved by not blocking projects · 20%+ improvement in search success · several hours less onboarding · documentation up to 10× faster versus manual writing.
10. Collaboration & human-in-the-loop suggestions
Stewards · business users
Without leaving the chat client, ask the assistant to create a comment on an object. The model can refine, translate, or adjust the tone, so people spend less time editing. This is the natural home for suggested fixes that a steward reviews before they become catalog truth.
How: create_comment
Value: stewardship happens where the conversation is; suggestions stay reviewable.
11. Direct attribute updates
Stewards
When the change is clear-cut and the user is authorised, apply an attribute change directly to an object — a one-step write-back from inside the client.
How: update_object
Value: small corrections land immediately, in the flow of work.
12. Context & tag propagation
Stewards
"Use the tags of the parent object for this child object."
How: get_ancestors · get_object_tags → update_object
Value: consistent classification; sensitivity and tags flow down the hierarchy without manual re-tagging.
13. Translation at scale
Stewards · business users
"Translate the Finance glossary term descriptions into German."
How: search_objects / get_object_details → update_object
Value: a multilingual option with consistent definitions across regions, without manual rewriting.
14. Full metadata pipeline: discover → analyse → enrich
Stewards
"Find undocumented tables in the Sales source, summarise their likely content, and propose descriptions."
How: get_data_sources → search_objects → get_object_details → update_object / create_comment
Value: turns the assistant into a documentation co-pilot; catalog completeness moves end to end.
15. Documentation completeness as a backlog
Stewards · data engineers
"Which Customer objects are missing an owner or a description?"
How: search_objects · get_object_details
Value: surfaces governance gaps proactively and gives stewards a prioritised backlog.
16. Emerging enrichment patterns
Stewards · data engineers
The same write-back foundation extends to more specialised jobs, each following the propose-then-review pattern:
- Duplicate detection — find objects that appear to describe the same thing and flag them for consolidation.
- Link suggestions — propose relationships between objects the assistant infers are connected.
- Semantic-layer generation — produce structured artifacts (e.g. a semantic-layer YAML file) from catalog context to feed downstream analytics tools.
Value: the AI proposes, a human disposes, and the catalog stays trustworthy.
Quick reference
| Use case | Pillar | Who | What happens |
|---|---|---|---|
| Self-service discovery | Use metadata | Business users, analysts | Natural-language question → grounded object & definition |
| Definitions in chat / Teams | Use metadata | Business users | Look up context inside everyday tools |
| Context in the IDE | Use metadata | Engineers, developers | Validate a column, trace a field before refactoring |
| Ground downstream AI | Use metadata | AI / app teams | Anchor other products in certified definitions |
| Impact analysis | Use metadata | Stewards, analysts | "What breaks if I change X?" via lineage |
| Onboarding & literacy | Use metadata | New joiners | Explain a dataset and its connections |
| Compliance & classification audit | Use metadata | DPO, auditors | AI scans for mis-classified / untagged PII & ownership gaps |
| Governance health check | Use metadata | Stewards | Detect tag / lineage inconsistencies, triage discussions |
| Description autogeneration | Enrich catalog | Stewards | Generate descriptions (single & batch) from context |
| Comments & suggestions | Enrich catalog | Stewards, business users | Post a reviewable suggestion from the client |
| Direct attribute updates | Enrich catalog | Stewards | Write a clear-cut change straight back |
| Tag / context propagation | Enrich catalog | Stewards | Flow parent tags down to child objects |
| Translation at scale | Enrich catalog | Stewards | Translate glossary descriptions, write back |
| Discover → analyse → enrich | Enrich catalog | Stewards | Find gaps, draft content, enrich end to end |
| Documentation completeness | Enrich catalog | Stewards | Surface missing owners / descriptions |
| Duplicates, links, semantic layer | Enrich catalog | Stewards, engineers | Specialised propose-then-review patterns |
The cross-cutting payoff
Whichever pillar a use case sits in, the same advantages run through all of them:
- Grounded answers — AI becomes your-context-aware, in a safe and predictable way.
- Less context switching — the catalog shows up inside the tools people already use.
- Governance-aligned and auditable — access control is inherited; every interaction is traceable.
- One reliable source of metadata for every assistant — connect once, reuse everywhere.
The two goals behind it are equally simple: increase catalog completeness, and increase usage by simplifying search and bringing the catalog closer to users. Pillar 1 drives the second, Pillar 2 drives the first — and because both run through the same governed server, every gain in one reinforces the other.