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Connect to a data source, profile its schema and quality, and recommend appropriate analyses. Use when starting work with a new dataset.
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Analyze an A/B test experiment and provide a clear ship/iterate/no-ship recommendation. Use when evaluating experiment results.
Run correlation analysis on connected data, identify strong relationships, and suggest regression models. Use when exploring relationships between variables.
Discover natural groupings in data using clustering algorithms with evaluation and visualization. Use when segmenting data or finding patterns.
Run an end-to-end data analysis pipeline — connect, profile, analyze, and report. Use when performing a complete analysis workflow.
Analyze a time series and generate forecasts with confidence intervals. Use when predicting future values from historical data.
Explore a graph or network dataset — structure, statistics, paths, and visualization. Use when working with DOT, GML, GraphML, or Mermaid files.
Based on SOC occupation classification
| name | explore-data |
| description | Connect to a data source, profile its schema and quality, and recommend appropriate analyses. Use when starting work with a new dataset. |
| allowed-tools | mcp__localdata__connect_database mcp__localdata__describe_database mcp__localdata__execute_query mcp__localdata__get_data_quality_report mcp__localdata__describe_table |
| argument-hint | <file-path or connection-string> |
Connect to a dataset, profile its structure and quality, and recommend what analyses to run next.
Connect to the data source. Call connect_database with the path or connection string from $ARGUMENTS. Note the assigned database name in the response.
Describe the schema. Call describe_database with the database name. Record the list of tables, column names, and column types. Identify which columns are numeric, categorical, datetime, and text.
Sample rows from each table. For each table (or the first 3 if many), call execute_query with SELECT * FROM <table> LIMIT 10. Inspect the returned rows to understand value ranges, formats, and potential join keys.
Describe key tables. For the most important tables (largest or most-referenced), call describe_table to get detailed column statistics including cardinality, null counts, and value distributions.
Run a quality report. Call get_data_quality_report with the database name. Review completeness, uniqueness, and consistency scores. Flag columns with high null rates (above 20%) or low cardinality that may need attention.
Summarize findings. Present a structured summary:
Recommend next analyses. Based on data characteristics, suggest specific next steps:
/localdata-mcp:analyze-correlations/localdata-mcp:forecast/localdata-mcp:cluster-analysis/localdata-mcp:regression/localdata-mcp:ab-test/localdata-mcp:graph-explore