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MCP for Data Analytics

Overview​

Data analysts use Glean's MCP server to query structured data sources, combine quantitative and qualitative insights, and generate reports without switching between multiple analytics tools.

Prerequisites​

Recommended connectors:

  • Databricks (for data warehouse queries)
  • Salesforce (for CRM data)
  • Google Drive (for spreadsheets and reports)
  • Confluence or Notion (for analysis documentation)
  • Slack (for data discussions)
  • Jira (for project tracking)

Supported MCP hosts:

  • Claude Desktop
  • ChatGPT
  • Cursor (for data notebooks)
  • Any MCP-compatible interface

Use Cases​

1. Natural Language Database Queries​

Query structured data sources using natural language instead of SQL.

What it does:

  • Translates natural language to SQL queries
  • Executes queries against connected data warehouses
  • Formats results for easy interpretation
  • Identifies notable patterns or anomalies

Note: Requires Databricks Genie or similar connectors that support structured data queries.

2. Trend Analysis and Pattern Recognition​

Identify trends in business metrics and customer behavior.

What it does:

  • Queries time-series data
  • Identifies patterns and correlations
  • Combines quantitative data with qualitative context
  • Provides business interpretation

3. Automated Report Generation​

Generate recurring reports by pulling data from multiple sources.

What it does:

  • Aggregates data from multiple sources
  • Calculates key metrics automatically
  • Adds qualitative context from discussions
  • Formats for stakeholder consumption

4. Anomaly Detection in Financial Data​

Identify unusual patterns or errors in financial datasets.

What it does:

  • Analyzes financial data for outliers
  • Identifies reconciliation issues
  • Flags potential errors or fraud
  • Prioritizes audit focus areas

5. Customer Cohort Analysis​

Analyze customer behavior by cohort to understand retention and growth patterns.

What it does:

  • Groups customers by cohort
  • Calculates retention and lifetime value metrics
  • Compares cohort performance
  • Identifies successful acquisition strategies

6. Root Cause Analysis​

Investigate data anomalies by combining quantitative and qualitative sources.

What it does:

  • Combines quantitative metrics with qualitative context
  • Correlates changes with events (deployments, campaigns)
  • Searches for related discussions and issues
  • Proposes hypotheses for investigation

7. Competitive Benchmarking​

Analyze competitive data and market positioning.

What it does:

  • Aggregates competitive intelligence
  • Compiles market research and analysis
  • Quantifies competitive performance
  • Identifies positioning opportunities

8. Data Quality Assessment​

Audit data quality and identify gaps or inconsistencies.

What it does:

  • Identifies incomplete or inconsistent data
  • Finds duplicates and errors
  • Quantifies data quality issues
  • Prioritizes remediation efforts

9. Predictive Analysis Support​

Gather data and context for predictive modeling.

What it does:

  • Aggregates historical data for modeling
  • Surfaces domain knowledge from past analyses
  • Suggests relevant features and variables
  • Connects quantitative data with qualitative insights

10. Ad Hoc Business Questions​

Answer urgent business questions quickly with data.

What it does:

  • Quickly queries relevant data sources
  • Provides evidence-based answers
  • Combines data with contextual information
  • Formats insights for executive consumption

Best Practices​

Start with Clear Questions​

✅ "What's the month-over-month growth in user sign-ups?"
✅ "Which customer segment has the highest churn rate?"
❌ "Tell me about our customers" (too broad)

Specify Time Ranges​

Always include time boundaries: "last quarter", "YTD", "since January 2024"

Combine Quantitative and Qualitative​

Don't just query numbers. Also search for context in Slack discussions,
meeting notes, and analysis docs to understand the "why" behind the data.

Validate Results​

When Glean returns data, cross-check key figures against known sources or
dashboards before using in reports.

Document Assumptions​

Ask Glean to note any assumptions, filters, or data limitations in the
analysis so stakeholders understand the context.

Troubleshooting​

Can't query structured data?

  • Verify Databricks Genie or similar connector is properly configured
  • Check that your user has query permissions on the data warehouse
  • Ensure the data source is actively indexed

Inaccurate calculations?

  • Be explicit about formulas: "Calculate as (new - old) / old * 100 for growth rate"
  • Specify how to handle nulls, duplicates, or edge cases
  • Ask Glean to show the query it's using so you can verify

Missing business context?

  • Connect Slack channels where data discussions happen
  • Index analysis documentation from Confluence or Notion
  • Include links to past analyses and reports

Results don't match dashboards?

  • Check if time zones or date boundaries are defined consistently
  • Verify filters and segments match your dashboard definitions
  • Confirm you're querying the same underlying data sources

See also​

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