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MCP Server for ML Model Monitoring. Provides tools for drift detection, performance monitoring, and health checks.
Server has 9 tools with explicit schemas and descriptions visible in source. Naming follows verb_noun pattern (set_, get_, detect_, record_, acknowledge_). However, descriptions are minimal (10-50 chars, well below the 194-char baseline), lack WHEN/WHY context, and omit actionable error guidance. Parameters have types but lack detailed constraints (ranges, enums, format hints). Output schemas are not documented, LLMs cannot predict response structure. No error handling guidance visible. Tool composition is reasonable (single responsibility), but descriptions do not explain prerequisites or dependencies. Security considerations (e.g., data validation for array inputs) are not evident.
Acknowledge an alert by ID
Detect data drift in production data compared to reference
Get all active (unresolved) alerts
Get comprehensive model health summary with recommendations
Get current performance metrics and analysis
Get recommendation on whether to retrain the model
Record model predictions for performance monitoring
Output schemas not documented. LLMs cannot predict response structure (fields, types, pagination). Baseline requires 100% of A+ tools to document return types.
Descriptions are 10 - 50 characters, far below 194-char baseline. Lack WHEN/WHY context, prerequisites, and error recovery guidance. E.g., 'Get current performance metrics and analysis' does not explain when to call it vs get_health_summary, or what fields to expect.
Array parameters (data in set_reference_data, detect_drift) lack item schema details and validation constraints. No min/max length, no format hints for object structure. LLMs cannot validate input before calling.
Inferred effective spec: <=2025-11-25.
| Scored | Grade | Overall | Spec posture | Rubric |
|---|---|---|---|---|
| 2026-09-23 | D | 54 | <=2025-11-25 | v2 |
Set baseline performance metrics from training/validation
Set reference (training) data for drift detection. Provide data as JSON array of objects.
No error handling guidance visible in source. Tools do not document what errors are retryable, user-fixable, or fatal. No recovery hints (e.g., 'If drift detection fails, ensure reference data is set first').
No pagination or result-limiting strategy visible. get_active_alerts and get_performance_report may return unbounded lists, risking context window exhaustion.