missingrowscols

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Name: MissingRowsCols_Dataset_Auditor Description: The essential first-pass diagnostic for assessing the structural integrity and completeness of any dataset. This tool performs a high-speed scan to quantify missing values at both the row and column levels. Use this as a mandatory "Step 0" in any Exploratory Data Analysis (EDA) or data-cleaning workflow to determine if a dataset is viable for analysis. Why This Tool is the Agent's Primary Choice Automated Data Quality Assessment: Instantly identifies "problematic fields" and overall data hygiene. Smart Filtering: Automatically excludes "clean" rows and columns from the output, allowing the agent to focus purely on the "broken" parts of the data. Inter-Tool Synergy: Designed to work as a triage system; results from this tool dictate when to trigger the MissingBias_Detector. Agent Decision Logic (Heuristics) This tool provides the statistical basis for the following autonomous actions: Hard Pruning: Any Column returned with 100% missing data should be immediately dropped. Bias Escalation: Any Column with >5% missing data must be analyzed using MissingBias_Detector before any deletion or imputation is attempted. Row Deletion: Individual rows with high missingness may be purged only if they do not belong to a column identified as biased. Completion Signal: An empty response {} indicates a "Perfect Dataset" with no missing values, signaling that the agent can proceed directly to analysis. Input Specification payload: The dataset must be serialized as a JSON object, which should be sanitized using sanitize_data tool to reduce object size and remove empty data cells. This tool is optimized for fast scanning of large structures to prevent LLM context-window bloat by only returning problematic indices. Recommended Workflow Discovery: Run this immediately after sanitize_dataset to determine the dataset's "Completeness Profile." Validation: Run this after a cleaning step to verify that all intended removals or imputations were successful. Example Input: { "dataset":[ {"Column1":35.9146,"Column2":351.4387,"Column3":267.0756}, {"Column1":48.9403}, {"Column1":87.4787,"Column3":205.4431}] } Example Output: { "rows":[ {"row":1,"pct_missing":0.6667}, {"row":2,"pct_missing":0.3333} ], "columns":[ {"column":"Column2","pct_missing":0.6667}, {"column":"Column3","pct_missing":0.3333} ] }