/explore-data - Profile and Explore a Dataset

If you see unfamiliar placeholders or need to check which tools are connected, see CONNECTORS.md.

Generate a comprehensive data profile for a table or uploaded file. Understand its shape, quality, and patterns before diving into analysis.

Usage

/explore-data <table_name or file>

Workflow

1. Access the Data

If a data warehouse MCP server is connected:

1. Resolve the table name (handle schema prefixes, suggest matches if ambiguous)
2. Query table metadata: column names, types, descriptions if available
3. Run profiling queries against the live data

If a file is provided (CSV, Excel, Parquet, JSON):

1. Read the file and load into a working dataset
2. Infer column types from the data

If neither:

1. Ask the user to provide a table name (with their warehouse connected) or upload a file
2. If they describe a table schema, provide guidance on what profiling queries to run

2. Generate Data Profile

Run the following profiling checks:

Table-level metrics:

• Total row count
• Column count and types breakdown
• Approximate table size (if available from metadata)
• Date range coverage (min/max of date columns)

Column-level metrics for each column:

• Data type (and whether it matches expected type)
• Null count and null rate (%)
• Distinct count and cardinality (distinct / total)
• For numeric columns: min, max, mean, median, stddev, percentiles (p25, p50, p75, p95, p99)
• For string columns: min/max length, most common values (top 10), empty string count
• For date/timestamp columns: min, max, distribution by time period
• For boolean columns: true/false/null distribution

Present the profile as a clean summary table, grouped by column type (dimensions, metrics, dates, IDs).

3. Identify Data Quality Issues

Flag potential problems:

• High null rates: Columns with >5% nulls (warn), >20% nulls (alert)
• Low cardinality surprises: Columns that should be high-cardinality but aren't (e.g., a "user_id" with only 50 distinct values)
• High cardinality surprises: Columns that should be categorical but have too many distinct values
• Suspicious values: Negative amounts where only positive expected, future dates in historical data, obviously placeholder values (e.g., "N/A", "TBD", "test", "999999")
• Duplicate detection: Check if there's a natural key and whether it has duplicates
• Distribution skew: Extremely skewed numeric distributions that could affect averages
• Encoding issues: Mixed case in categorical fields, trailing whitespace, inconsistent formats

4. Suggest Interesting Dimensions and Metrics

Based on the column profile, recommend:

• Best dimension columns for slicing data (categorical columns with reasonable cardinality, 3-50 values)
• Key metric columns for measurement (numeric columns with meaningful distributions)
• Time columns suitable for trend analysis
• Natural groupings or hierarchies apparent in the data
• Potential join keys linking to other tables (ID columns, foreign keys)

5. Recommend Follow-Up Analyses

Suggest 3-5 specific analyses the user could run next:

• "Trend analysis on [metric] by [time_column] grouped by [dimension]"
• "Distribution deep-dive on [skewed_column] to understand outliers"
• "Data quality investigation on [problematic_column]"
• "Correlation analysis between [metric_a] and [metric_b]"
• "Cohort analysis using [date_column] and [status_column]"

Output Format

## Data Profile: [table_name]

### Overview
- Rows: 2,340,891
- Columns: 23 (8 dimensions, 6 metrics, 4 dates, 5 IDs)
- Date range: 2021-03-15 to 2024-01-22

### Column Details
[summary table]

### Data Quality Issues
[flagged issues with severity]

### Recommended Explorations
[numbered list of suggested follow-up analyses]

Tips

• For very large tables (100M+ rows), profiling queries use sampling by default -- mention if you need exact counts
• If exploring a new dataset for the first time, this command gives you the lay of the land before writing specific queries
• The quality flags are heuristic -- not every flag is a real problem, but each is worth a quick look