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Oura MCP

by link.smirnov.mcp-oura
OverviewDiscussionsAdmin

Date Range

get_date_range
Read-onlyIdempotent

Get a single metric over a date range for trend analysis. Use this when the user asks about trends over multiple days like 'how was my sleep this week' or 'show me my HRV over the past month'. Supports daily active_calories and total_calories (kcal) as well as steps and scores. For a single-day snapshot, use get_daily_summary.

Input Schema

NameRequiredDescriptionDefault
endYesEnd date in strict YYYY-MM-DD (user's local date). Max 180 days from start.
startYesStart date in strict YYYY-MM-DD using the user's LOCAL date (Oura attributes data to local days).
metricYesWhich metric to project over the range. sleep_score/total_sleep_hours/efficiency_pct come from daily_sleep + sleep endpoints; readiness_score/hrv/resting_hr come from daily_readiness; activity_score/steps/active_calories/total_calories (kcal) from daily_activity.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changed
    • changedInput schema / $schema
      Previous value: -"http://json-schema.org/draft-07/schema#"New value: +"https://json-schema.org/draft/2020-12/schema"
  2. Changed2 schema fields changed
    • changedInput schema / properties / metric / description
      Previous value: -"Which metric to project over the range. sleep_score/total_sleep_hours/efficiency_pct come from daily_sleep + sleep endpoints; readiness_score/hrv/resting_hr come from daily_readiness; activity_score/steps from daily_activity."New value: +"Which metric to project over the range. sleep_score/total_sleep_hours/efficiency_pct come from daily_sleep + sleep endpoints; readiness_score/hrv/resting_hr come from daily_readiness; activity_score/steps/active_calories/total_calories (kcal) from daily_activity."
    • changedInput schema / properties / metric / enum
      Previous value: -[
-  "sleep_score",
-  "readiness_score",
-  "activity_score",
-  "hrv",
-  "resting_hr",
-  "steps",
-  "total_sleep_hours",
-  "efficiency_pct"
-]New value: +[
+  "sleep_score",
+  "readiness_score",
+  "activity_score",
+  "hrv",
+  "resting_hr",
+  "steps",
+  "active_calories",
+  "total_calories",
+  "total_sleep_hours",
+  "efficiency_pct"
+]
  3. First observed

TDQS

A4.4/5.0
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare readOnlyHint, idempotentHint and openWorldHint, so safety is covered. The description adds real value beyond them by disclosing which metrics are available and which underlying endpoints they come from, though it omits return shape (series of daily points) and any error/empty-data behavior.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Three tight sentences with zero filler: purpose first, then usage examples, then the routing rule to the sibling. Every sentence earns its place.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a read-only projection tool with full schema coverage and no output schema, the description covers purpose, triggers, supported metrics and the alternative. The main remaining gap is not saying what the response looks like (one value per day, ordering, gaps), which matters for 'trend analysis'.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the parameters' formats, local-date semantics and the 180-day cap are already fully documented. The description only re-states metric availability and clarifies kcal units, which is a marginal add over the schema's own endpoint mapping.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb and resource ('Get a single metric over a date range') plus its analysis purpose (trend analysis). It explicitly contrasts with the sibling get_daily_summary for single-day snapshots, so an agent can pick correctly without inspecting schemas.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Gives concrete user-question triggers ('how was my sleep this week', 'show me my HRV over the past month') and names the alternative with the condition that selects it ('For a single-day snapshot, use get_daily_summary'). Nothing essential is left to inference.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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