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.
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 / search_objects (with filters) → get_object
Value: the catalog meets people where they are; adoption rises because there is only one interface to learn.
Definitions in the flow of work (chat and Teams)
Business users
Employees look up definitions and object information without leaving the tool they live in all day.
Example. A leisure and tourism operator surfaces catalog definitions inside Microsoft Teams, so business users ask in natural language with no portal and no extra login. A regional insurance carrier took the same route, adding natural-language catalog access — and the ability to leave feedback — directly in Teams.
Value: trusted context appears inside everyday tools, and every answer is permission-aware.
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_object_relations (relation: links, ancestors) · get_object
Value: no toggling between a documentation portal and the code editor — the definition sits next to the code.
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.
Example. A large multi-brand insurance group pursues self-service analytics by pairing catalog context with a query engine: the catalog explains the structure and meaning of the data, a model builds the query from that context, and the engine executes it — so a non-technical user goes from a business question to a result without hand-writing SQL.
How: semantic_search · get_object
Value: trustworthy, governance-aligned answers inside the products your people and customers actually use.
Impact analysis
Stewards · analysts · data engineers
"If we change the Orders table schema, which dashboards and reports break?"
How: downstream traversal via get_object_relations (relation: links; multi-hop chains may need iterative lookups)
Value: faster, explainable impact assessment grounded in real lineage.
Onboarding and data literacy
New joiners · business users
"Explain the Customer 360 dataset and how it connects to the CRM."
How: get_object · get_object_relations (relation: links)
Value: self-serve ramp-up that reduces reliance on tribal knowledge — several hours less to onboard a new user.
Compliance and 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.
What you get back: not a raw list, but a prioritised review queue — "3 objects probably hold PII but aren't tagged; 2 PII objects have no owner" — each with the reason it was flagged. With one more step it can post each finding as a comment for the steward to confirm.
How: list_workspaces_and_versions · search_objects · get_object · 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.
Governance health check and 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.
What you get back: a short governance health report — the inconsistencies worth fixing, the stale or conflicting definitions, and the three discussions to act on first — instead of reading tasks and tags one object at a time.
How: get_object_relations (relation: ancestors, links) · get_object · 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.
Safe by design. Enrichment can be direct (an attribute change written straight back via update_object) or human-in-the-loop (a suggestion posted as a comment for a steward to review first). Either way, stewards stay in control, everything stays editable, and every change is auditable.
Description autogeneration — single and 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.
Example. A global retail brand fills its catalog with context before opening it to business users — generating descriptions from its MCP client (seeded with existing metadata plus internal documentation), then reviewing and refining.
How: get_data_sources / search_objects → get_object → update_object
Value and 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.
Collaboration and 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.
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.
Context and tag propagation
Stewards
"Use the tags of the parent object for this child object."
How: get_object_relations (relation: ancestors) · get_object → update_object
Value: consistent classification; sensitivity and tags flow down the hierarchy without manual re-tagging.
Translation at scale
Stewards · business users
"Translate the Finance glossary term descriptions into German."
How: search_objects / get_object → update_object
Value: a multilingual option with consistent definitions across regions, without manual rewriting.
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 → update_object / create_comment
Value: turns the assistant into a documentation co-pilot; catalog completeness moves end to end.
Documentation completeness as a backlog
Stewards · data engineers
"Which Customer objects are missing an owner or a description?"
How: search_objects · get_object
Value: surfaces governance gaps proactively and gives stewards a prioritised backlog.
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.