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Diabetes summary for a window

get_diabetes_summary

Get a complete diabetes summary for any date range: glucose control, time in range, insulin, carbs, and best/worst days. Ideal for reviewing daily, weekly, or monthly trends.

Instructions

The single best starting point for any overview question ("how was my control yesterday / over the last 3 weeks / last 6 months"). Returns fixed-size aggregates no matter how long the span, so it is cheap to call over months and tolerates very long windows.

TIP: because this tool is uncapped, a deliberately wide call (e.g. start 2000-01-01T00:00:00.000Z, end tomorrow) is the quickest way to discover how much data the system actually holds: the returned reportRange.start and reportRange.end are the first and last readings present in the archive. Use it as an orientation call before drilling into a specific period.

Insulin uses the project-wide rule: bolus is summed from individual events; basal comes from Glooko's per-day totals. The basal/bolus split is reported as percentages on a per-day-rate basis (a useful balance metric for a closed-loop system). GMI and CV are computed from the CGM readings.

Best/worst day and hour are ranked decisively: Time In Range first, then closeness to the glucose target in force at each reading (median absolute deviation), then variability, and each carries those figures so the ranking is explainable.

Returns: reportRange (start, end, days, reflecting the actual data present), glucoseControl (averageBG, gmiEstimatedA1c, stdDev, coefficientOfVariation, variability flag, timeInRange/timeLow/timeHigh, cgmReadingCount); glucoseExtremes (highest and lowest readings, each with every timestamped instance); bestWorst (bestDay, worstDay, bestHour, worstHour, each with tir, medianAbsTargetDev, cv); insulin (observedDays, bolusUnits, bolusUnitsPerDay, bolusEventCount, avgUnitsPerBolus, and when Glooko daily data exists basalUnits, basalDayCount, averageBasalUnitsPerDay, basalPercent, bolusPercent); bolusArchitecture (counts by bolus type); carbs (carbsGrams, carbsPerDay, carbEntryCount); and settings (the time-segmented profiles in force). All timestamps are plain wall clock time (see start/end parameter notes), not UTC.

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

A4.4/5.0
Behavior5/5

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

With no annotations, the description carries the full behavioral burden and does so exceptionally well. It discloses fixed-size output, cheap long-window calls, uncapped orientation behavior, insulin aggregation rules, tie-breaking logic for ranking, and the plain-wall-clock timezone convention. This gives an agent a reliable model of how the tool behaves.

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

Conciseness4/5

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

The description is long but its length is largely earned: it front-loads purpose, adds a practical discovery tip, then explains computational caveats and the return contract, which is necessary because there is no output schema. A few parenthetical asides are slightly expendable, but overall it is well-ordered and information-dense.

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?

For a complex aggregation tool with no annotations and no output schema, this description is remarkably complete. It explains the full set of returned fields, how aggregates are computed, how rankings are resolved, and the timezone convention. An agent has everything it needs to invoke the tool correctly and interpret the result.

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 description reinforces the wall-clock timestamp caveat and refers to the parameter notes, but the schema already documents defaults, overrides, and formats for all five parameters. No additional parameter meaning is needed.

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 first sentence names the tool as 'the single best starting point for any overview question' and specifies that it returns fixed-size aggregates over a window. It clearly positions itself as distinct from the sibling period-specific tools by framing itself as an orientation call before drilling into a specific period.

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 recommends using this tool for overview questions and as an orientation call to discover how much data the system holds before drilling into a specific period. It does not explicitly name alternatives or give when-not-to-use conditions, but the usage context is strongly established.

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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