With a passion for turning data complexity into clarity, Jessica Sandifer is an experienced content manager who crafts stories that resonate across technical and business audiences. At DataGalaxy, she creates content and product marketing messages that demystify data governance and make AI-readiness actionable.
Your AI assistant doesn’t know your data (but it should)
You’re working with Claude or another AI assistant on a customer analysis. You ask it to find the right data tables. It suggests tables, sounds confident, and may even generate SQL.
The problem: the AI doesn’t know that half those tables are deprecated. It doesn’t know that “customer_master” failed its last quality check. It can’t tell you that the “revenue” field has three different definitions across your organization, and you’re looking at the wrong one.
Your AI is smart, but it’s blind to what makes your data usable: governance rules, quality scores, business definitions, and the “don’t use this table, use that one instead” knowledge your team has built over years.
Until now.
DataGalaxy MCP server: bringing context to AI
DataGalaxy launched an MCP server that changes how AI tools work with your data catalog. Instead of guessing, your AI can:
- Search your catalog intelligently
Find datasets, fields, reports, and processes by name, type, description, or module - Check data lineage
Understand where data comes from and how it flows through your systems - Verify ownership and stewardship
Know who owns what data, who the stewards are, and who to contact - Access business context
Read comments, tasks, and descriptions that explain what data means and how to use it - Filter by relevance
Find recently created or modified assets, or search within specific modules (Glossary, Dictionary, Processing, Diagrams, Uses) - Understand data sources and technologies
Know which systems your data lives in and what technologies power them
Your AI can now check DataGalaxy first. It knows which data is certified, who owns what, where data comes from, and all the context that matters.
What is MCP (the simple version)
MCP stands for Model Context Protocol.
Think of it like this: before MCP, every AI tool was an island. It could only work with what you told it in the conversation. Now, with MCP, AI tools can plug into external systems and pull information when they need it.
It’s like the difference between texting someone facts manually versus letting them look things up themselves.
The old world: AI works alone
You: “Find me customer data tables”
↓
AI: “Here are some guesses based on common patterns…”
(AI has no way to check your actual catalog)
The new world: AI can check your catalog
You: “Find me certified customer data tables”
↓
AI → checks DataGalaxy via MCP
↓
AI: “I found 3 certified tables: customer_360 (quality 95%, owned by data team, refreshed daily)…”
What this actually means for your team
Data discovery becomes conversational
Your analysts can ask questions in plain English and get real answers from your catalog, not hallucinations.
- “Show me all the customer tables that are approved for machine learning”
- “What does ‘active user’ mean in our company?”
- “Where does the revenue number in this dashboard come from?”
This is not magic. The AI simply has access to the same information your data team has in DataGalaxy.
Impact analysis gets easier
Your data engineers can ask:
- “What breaks if I change this field?”
The AI queries DataGalaxy’s lineage and tells them exactly which dashboards, reports, and models depend on that data.
No more spreadsheet detective work. No more breaking production dashboards by accident.
Governance actually gets used
Governance only works if people follow it. And people only follow it if it’s easy.
When your AI assistant automatically filters for certified, compliant, high-quality data, governance stops being a blocker and starts being a guide.
The AI naturally steers people toward trusted data because it sees quality scores and certification flags.
Onboarding gets way faster
New team members working with an AI assistant that has DataGalaxy access get institutional knowledge automatically.
No need to ask where things are or what terms mean. The AI already knows where to look.
No more tool switching
You do not need to switch between tools. DataGalaxy is brought directly where it is needed.
It’s like having a senior team member who knows exactly where everything is and never gets tired of explaining.
How it works (without getting too technical)
Step-by-step flow
- You ask your AI assistant a question about your data
- The AI recognizes it needs information from DataGalaxy
- The AI calls the DataGalaxy MCP server
- The MCP server queries your DataGalaxy catalog:
- Search by name, type, description, or module
- Retrieve ownership and stewardship
- Pull comments, tasks, and definitions
- Fetch data sources, technologies, and lineage
- The AI receives real context
- The AI gives you an accurate, grounded answer
Result: answers based on your actual data governance, not guesses.
Why DataGalaxy’s approach is different
When AI connects to DataGalaxy, it does not just get a list of tables. It gets:
Real governance context
- Data owners, stewards, and experts
- Tags and classifications
- Comments and tasks
- Creation and modification timelines
Business semantics
- Official definitions from Glossary and Dictionary
- Business meaning, not just technical metadata
- Module-aware searches
Collaboration and knowledge
- Team discussions in comments
- Quality feedback and usage notes
- Tasks showing what needs attention
- The “why” behind decisions
Strategic alignment
- Connections across Diagrams, Processing, and Uses
- Relationships between reports, dashboards, and datasets
- Full data flow visibility
The difference that matters
When your AI knows:
- “Not for production use” vs “certified by governance team”
- Who the real data owner is
- Which definition is the right one
That is the difference between guessing and knowing.
Real scenarios where this changes everything
Scenario 1: the analyst
Before: searches Confluence, asks Slack, waits, validates, starts analysis after days.
Now: asks AI, gets certified datasets, ownership, and context instantly. Starts in minutes.
Scenario 2: the engineer
Before: maps dependencies manually and breaks dashboards.
Now: asks AI, sees lineage and dependencies, coordinates safely.
Scenario 3: the data scientist
Before: uses non-compliant data and delays the project.
Now: AI filters compliant datasets automatically and the project ships on time.
Scenario 4: the new hire
Before: weeks of onboarding and scattered knowledge.
Now: AI answers everything from the catalog and productivity ramps in days.
What works with this
The MCP server works with any MCP-compatible tool:
- Claude Desktop
- Cursor
- OpenAI
- GitHub Copilot
- Claude.ai
- Windsurf
- Microsoft Copilot Studio
- Any MCP-compatible client
The ecosystem is growing fast.
Security (yes, we thought about it)
- Your data stays in DataGalaxy
- No caching or duplication
- Token-based authentication
- Same permission model
- Encrypted traffic
- Full audit trail
If a user cannot access data in DataGalaxy, neither can their AI.
Getting started
- Generate an API token
- Connect your AI tool
- Enable MCP server
Most teams are up and running in under an hour.
Why this matters more than it seems
AI tools have been powerful, but blind.
They could write SQL and generate code, but they could not understand your data or know which tables to trust.
MCP changes that.
AI becomes context-aware. It checks before suggesting. It understands your data landscape.
Because MCP is an open standard, your data catalog becomes the source of truth for any AI tool.
What’s next
This is just the beginning.
Today: search, tags, collaboration via comments.
Tomorrow: deeper governance integration, tighter AI workflows, governed data as the default path.
Final thought
AI is only as good as the context it has. And your context lives in DataGalaxy.
Want to see it in action?
Book a demo with our team and see how your AI assistant becomes data-catalog-aware in minutes.
