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Hourly (circadian) trends

get_hourly_trends

Aggregate glucose readings by clock-hour, showing average BG, time-in-range, and low/high counts per hour to identify recurring circadian patterns like dawn phenomenon and evening highs.

Instructions

Time In Range and average glucose pooled by clock-hour across the whole window, so every reading that fell in the 07:00 hour on any day is combined into one 07:00 row, and so on for all 24 hours.

Use it for "why am I always high/low at a certain time" questions, recurring circadian patterns, the dawn phenomenon and evening highs.

Hours are the device's own wall-clock hour (not UTC) — this already IS the patient's local hour at the time each reading was taken, so present it as-is with no conversion.

Returns: a byHour array of up to 24 rows, each with hour (wall clock, "HH:00"), averageBG, timeInRange, timeLow, timeHigh and the reading count for that hour. Glucose values are in the configured unit.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
endYesRequired. Window end as an ISO 8601 timestamp, e.g. 2026-06-20T00:00:00.000Z — plain wall clock time, same caveat as start (the "Z" is a format artifact, not a UTC claim). Treated as inclusive and must be after start. All timestamps returned by this API are likewise plain wall clock time, unconverted.
lowerNoOptional. Low (hypo) boundary in the chosen unit; readings below it count as time-low. Omit to use the server default (OMNI_LOWER). Pass only to override for this one call, e.g. to ask about time under a different threshold.
startYesRequired. Window start as an ISO 8601 timestamp, e.g. 2026-06-19T00:00:00.000Z. IMPORTANT: despite the trailing "Z", this is plain WALL CLOCK time, not true UTC — Glooko records only the literal date/time the patient's device showed, with no timezone attached. Use the patient's own wall-clock digits directly (no conversion): resolve "yesterday" or "last 3 weeks" straight into the matching wall-clock date and time. Treated as inclusive.
unitsNoOptional. Glucose unit for this call. Omit to use the unit configured on the server (OMNI_UNITS). One of: "mmol" (mmol/L) or "mgdl" (mg/dL). Pass only to override the configured unit for this one call.
upperNoOptional. High (hyper) boundary in the chosen unit; readings above it count as time-high. Omit to use the server default (OMNI_UPPER). Pass only to override for this one call.
Install Server

TDQS

A4.5/5.0
Behavior5/5

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

With no annotations, the description carries the full burden, and it delivers: it explains the pooling semantics, the critical wall-clock vs UTC nuance, that hours are presented as-is without conversion, and the exact return fields. This is unusually transparent about a subtle timezone behavior that could easily cause misuse.

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?

The description is compact and well-structured: core behavior first, then use cases, then the timezone warning, then return format. Every sentence earns its place and no information is redundant with the schema.

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

Completeness5/5

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

Given no output schema and no annotations, the description is remarkably complete. It covers what the tool computes, when to use it, the timezone interpretation, the unit convention, and the structure of the byHour result. An agent has enough to invoke it correctly without resorting to guesses.

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 is 3. The input schema fully documents start, end, lower, upper, and units. The description reinforces the window concept and timezone caveat already present in the schema but does not add new parameter-level information.

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 precise definition: 'Time In Range and average glucose pooled by clock-hour across the whole window', naming both the resource and the aggregation behavior. It clearly distinguishes itself from ordinary time-series tools by emphasizing that all readings from the same wall-clock hour across days are combined into a single row.

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

Usage Guidelines4/5

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

The description explicitly lists concrete use cases: 'why am I always high/low at a certain time' questions, recurring circadian patterns, dawn phenomenon, and evening highs. It does not name sibling tools or say when not to use this tool, but the context is clear enough for an agent to select it appropriately.

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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