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robcerda

Oura MCP Server

by robcerda

get_sessions

Retrieve guided and unguided Oura app sessions (breathing, meditation, rest) with type, start/end time, and mood. Optionally include heart rate, HRV, and motion time series data.

Instructions

Get guided and unguided sessions from the Oura app (breathing, meditation, rest, etc.): type, start and end time, and mood. Set include_time_series for the heart rate, HRV and motion recorded during each session.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
end_dateNoLast day to include (inclusive), YYYY-MM-DD. Defaults to today.
next_tokenNoContinue a truncated result: pass the next_token from the previous response along with the same dates.
start_dateNoFirst day to include, YYYY-MM-DD. Defaults to 6 days before end_date.
include_time_seriesNoInclude the embedded time series (5 minute sleep phases, 30 second movement, per sample heart rate and HRV, MET samples). Off by default because they dominate the response size.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A3.8/5.0
Behavior3/5

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

No annotations exist, so the description carries the burden. It discloses the kind of data returned and that include_time_series pulls in heart rate, HRV, and motion, but it does not mention authentication, pagination/truncation, response size effects, or lack of side effects. These are meaningful gaps for a no-annotation tool.

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?

Two efficient sentences: the first establishes purpose and scope, the second highlights the one parameter worth calling out. No filler or repetition.

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?

With a rich input schema and an output schema present, the description covers the core semantics adequately: what a session is, what fields are returned, and the optional time series. It is slightly short on operational context like auth requirements and when to choose this over related tools, but those are secondary given the schema and sibling context.

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 baseline applies; the schema already documents date formats, defaults, and next_token semantics. The description adds only a light reference to include_time_series and does not improve on the schema's parameter explanations.

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?

The description opens with a specific verb and resource: 'Get guided and unguided sessions from the Oura app', and names concrete session kinds (breathing, meditation, rest) plus fields returned. This makes it easy to distinguish from sibling tools like get_workouts and get_sleep_periods.

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

Usage Guidelines3/5

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

The intended use is implied by naming the Oura session resource, and the second sentence gives an optional-parameter instruction, but there is no explicit when-to-use guidance or comparison to sibling alternatives such as get_workouts. The agent must infer which tool fits.

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