Model Context Protocol server for Oura Ring health data analysis and insights
This server has 14 tools with basic schemas and descriptions, but quality is inconsistent. All tools have explicit input schemas with type information and parameter descriptions, which is good. However, descriptions are uniformly generic (12-50 chars) and lack LLM-optimized guidance on WHEN to use each tool or WHAT distinguishes similar tools. No tool descriptions include recovery hints, dependencies, or error guidance. Output schemas are completely undocumented, LLMs cannot determine what fields to expect from any tool. Tools like 'generate_statistics_report', 'generate_daily_brief', 'generate_weekly_report' are poorly differentiated, an LLM cannot easily decide which to call. Parameter descriptions are minimal (e.g., 'Number of days to analyze' with no range constraints). No tools declare error handling, retryability, or side effects. All tools are READ_ONLY, which is good for safety, but this is not explicitly stated in descriptions. The server lacks pagination guidance, output field documentation, and composition patterns.
Analyze sleep trend.
Assess readiness for specific training type.
Find correlations between two metrics.
Detect current recovery status based on multiple signals.
Generate daily health brief.
Generate comprehensive statistics report across all metrics.
Output schemas completely undocumented. No tool documents what fields it returns or their types. LLMs cannot plan downstream tool calls or extract chaining IDs without guessing.
Tool descriptions are generic and do not guide LLM selection. All GET tools say only 'Get [resource]' without explaining when to use vs. similar tools, what distinguishes daily_brief from weekly_report, or what data preprocessing happens inside.
| Scored | Grade | Overall | Spec posture | Rubric |
|---|---|---|---|---|
| 2026-09-22 | F | 49 | 2026-07-28+ | v2 |
| 2026-03-09 | F | 0 | - | v1 |
Generate comprehensive weekly health report.
Get time-series heart rate data.
Get raw HRV values in milliseconds from the detailed sleep endpoint.
Get raw sleep data from Oura API for debugging.
Get detailed sleep sessions with exact times and durations.
Get detailed workout sessions.
Predict readiness scores for upcoming days.
Predict sleep quality for upcoming days based on historical patterns.
Three distinct report generation tools (daily_brief, weekly_report, generate_statistics_report) with overlapping names and unclear distinctions. An LLM cannot reason which to call for a specific use case.
Numeric parameter constraints missing. 'days' parameters have no documented range (minimum/maximum). An LLM could pass days=365000, days=0, or days=-1 without validation guidance.
No error handling guidance. Tools provide no description of error conditions, recovery steps, or when to retry. An LLM receives a failure and has no idea what caused it or what to try next.
Parameter 'training_type' in assess_training_readiness has no enum or documentation of valid values. LLM must guess what types are supported (e.g., 'running', 'gym', 'cycling').
Parameters 'metric1' and 'metric2' in correlate_metrics lack enum or description of valid metric names. LLM cannot determine what metric values are accepted.
No pagination support documented. If tools return large result sets (e.g., 30 days of hourly heart rate = 720+ records), no limit or offset parameters exist. Output context could explode without pagination.
No composition hints. Tools do not document which output fields feed into downstream tool parameters. e.g., does correlate_metrics return metric_id fields that other tools need?