explore-datalisted

# /explore-data - Profile and Explore a Dataset > If you see unfamiliar placeholders or need to check which tools are connected, see [CONNECTORS.md](../../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. Understand Structure Before analyzing any data, understand its structure: **Table-level questions:** - How many rows and columns? - What is the grain (one row per what)? - What is the primary key? Is it unique? - When was the data last updated? - How far back does the data go? **Column classification** — categorize each column as one of: - **Identifier**: Unique keys, foreign keys, entity IDs - **Dimension**: Categorical attributes for grouping/filtering (status, type, region, category) - **M