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Bucketed trend over any timeframe

get_trend

Compare glucose, insulin, and carb metrics across time buckets to identify trends and assess data reliability.

Instructions

Glucose, insulin and carb aggregates split into time buckets across a span, for "how have things changed month by month over the last year" style questions.

Each bucket is computed independently from the raw readings (not by averaging averages), so a year split by month returns 12 correct rows in a single call without pulling raw data back to you. Prefer this over making many separate summary calls for a multi-period comparison.

Insulin per bucket follows the same rule as elsewhere: bolus is summed from individual events; basal comes from Glooko's per-day totals. Each bucket also reports observedDays (the real decimal span of data in it) and a coverage percentage, so you can judge which rows to trust.

Returns: bucketCount and a buckets array. Each row has: bucket (period key), start, end, observedDays; glucose (avg, timeInRange, timeLow, timeHigh, stdDev, coefficientOfVariation, gmiEstimatedA1c, cgmReadingCount); insulin (bolusUnits, bolusUnitsPerDay, bolusEventCount, avgUnitsPerBolus, and when Glooko daily data exists basalUnits, basalDayCount, averageBasalUnitsPerDay, basalPercent, bolusPercent); carbs (carbsGrams, carbsPerDay, carbEntryCount); and coverage (cgmReadingCount, expectedReadingCount, coveragePercent, trustworthy).

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.
modeNoOptional (default: "calendar"). How the span is divided into buckets. "calendar" uses real calendar units (days/weeks/months/quarters) with ragged edges at the ends; "fixed" uses equal-length buckets of fixedSizeDays counting from the start date. Choose the bucket size with "granularity" (calendar) or "fixedSizeDays" (fixed).calendar
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.
granularityNoOptional (default: "month"). Calendar bucket size. Only used when mode is "calendar". One of: "day", "week", "month", "quarter".month
fixedSizeDaysNoOptional (default: 7). Length of each bucket in days. Only used when mode is "fixed".
Install Server

TDQS

A4.7/5.0
Behavior5/5

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

With no annotations provided, the description carries the full burden, and it delivers: it explains that buckets are computed independently rather than by averaging averages, details insulin aggregation rules for bolus versus basal, and discloses observedDays/coverage percentages so the agent can judge trustworthiness. It also describes the exact return shape, which is critical given no output schema.

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 long but every section earns its place: use-case framing, computation semantics, insulin rules, trust metrics, and a complete return-field listing. It is front-loaded with the primary purpose and avoids filler or repetition.

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 the tool's complexity, 8 parameters, and no output schema, the description is unusually complete. It documents the full return structure, covers edge semantics like independence of buckets and observedDays trust metrics, and complements the schema's timezone caveats and parameter documentation. Nothing essential for correct invocation appears missing.

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 coverage is 100%, with all parameters, defaults, enums, and units already documented in the input schema. The description adds useful context about bucket independence and returned fields, but it does not materially expand parameter-level meaning beyond what the schema already provides, so the baseline of 3 applies.

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 uses a specific verb and resource: it returns glucose, insulin, and carb aggregates split into time buckets across a span. It clearly distinguishes this from other tools by framing it as a multi-period trend comparison, and the title reinforces the bucketed trend concept.

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?

It explicitly says to prefer this tool over making many separate summary calls for multi-period comparison. It explains the benefit — 12 correct rows in a single call without pulling raw data — which gives an agent a concrete decision rule for when this tool is appropriate.

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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