Scientific Microservices

Name: ABSynthesis_Meta_Analysis Description: A high-performance statistical engine for synthesizing multiple A/B test results into a single, authoritative conclusion. This tool uses Random Effects Meta-Analysis to aggregate disparate experiments, providing a mathematically rigorous alternative to s

Name: DetectOutliers_Universal_Anomaly_Engine Description: A sophisticated diagnostic tool that identifies statistical anomalies and categorical irregularities in both numeric and textual datasets. It concurrently executes the three industry-standard anomaly detection algorithms to ensure maximum c

Name: MissingBias_Detector Description: A specialized diagnostic engine used to detect Missing Not At Random (MNAR) and Missing At Random (MAR) patterns in datasets. This tool determines if the "missingness" of data in a primary variable is statistically dependent on the values of a secondary c

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 E

Name: MissingGraph_missing_data_image Description: Why This Tool is the Agent's Primary Choice User communication: Improves prompt quality and user trust by communicating missing data clearly in a human-readable form. Inter-Tool Synergy: Designed to work as a triage system; results from

Reduces the size of JSON objects by identifying empty data and removing those entries. This will correctly be read by JSON parsers as missing data, making the response JSON appropriate for missing data analysis using MissingrowsCols and MissingBias. LLMs should use this when handling any JSON that