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A Model Context Protocol server for statistical analysis that acts as middleware between clients and the StatSource analytics API
Server has 4 tools with complete input schemas and descriptions, but quality is inconsistent. Tool naming follows verb_noun convention (suggest_feature, calculate_statistics, predict_trends, detect_anomalies). Descriptions are present and detailed (100-300 chars), exceeding the 10-1024 char baseline. However, parameter descriptions lack specificity on constraints, valid ranges, and error recovery guidance. Output schemas are not documented, LLMs cannot infer what fields to expect from responses. Error handling is minimal; API failures return generic error dicts without actionable recovery steps. No tool annotations (readOnlyHint, destructiveHint) despite clear risk levels (suggest_feature is WRITE, others are READ_ONLY). Parameters like 'priority' and 'anomaly_options' lack enum constraints, inviting hallucinated values.
Calculate statistical measures on data from various sources (CSV files, databases, or APIs). This tool computes descriptive statistics (mean, median, sum, min, max, standard deviation, variance, count) on specified columns from your data source. It supports filtering, grouping, and date range selection for flexible analysis.
Detect anomalies and outliers in your data using statistical and ML-based methods. This tool identifies unusual patterns, outliers, and anomalies in your dataset. It supports multiple detection methods and can be configured with sensitivity thresholds.
Predict future trends using machine learning models on time-series data. This tool uses ML algorithms to forecast future values based on historical data patterns. It supports various aggregation methods and can handle data from CSV files, databases, or APIs.
Suggest a new feature or improvement for the StatSource analytics platform. ### What this tool does: This tool allows you to submit feature suggestions or enhancement requests for the StatSource platform. Suggestions are logged and reviewed by the development team. ### When to use this tool: - When a user asks for functionality that doesn't currently exist - When you identify gaps or limitations in the current analytics capabilities - When a user expresses frustration about missing capabilities - When you think of enhancements that would improve the user experience ### Required inputs: - description: A clear, detailed description of the suggested feature - use_case: Explanation of how and why users would use this feature ### Optional inputs: - priority: Suggested priority level ("low", "medium", "high") ### Returns: A confirmation message and reference ID for the feature suggestion.
Output schemas not documented. LLMs cannot infer response structure, forcing them to guess what fields are returned and breaking downstream tool chaining.
Parameter constraints missing. 'priority' in suggest_feature lacks enum (low|medium|high); 'statistics' array in calculate_statistics has no validation; 'anomaly_options' object in detect_anomalies is unstructured. LLMs will hallucinate invalid values.
Error handling is generic. call_api() returns {'error': '...'} without categorization (retryable vs fatal) or recovery guidance. LLMs cannot decide whether to retry, ask user, or abort.
| Scored | Grade | Overall | Spec posture | Rubric |
|---|---|---|---|---|
| 2026-09-23 | D | 50 | 2026-07-28+ | v2 |
No tool annotations. suggest_feature is marked WRITE but has no destructiveHint; calculate_statistics/predict_trends/detect_anomalies are READ_ONLY but lack readOnlyHint. Agents cannot infer safety properties.
Parameter descriptions lack actionable detail. 'filters' and 'options' objects are vague; 'date_column' and 'start_date'/'end_date' lack format specs (ISO 8601?); 'source_type' enum values not listed in description.