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rilhia

PodQuery

by rilhia
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Diabetes summary for a window

get_diabetes_summary

Retrieve a fixed-size diabetes overview for any date range, including glucose control, insulin, carbs, and best or worst days, to start analysis or check data coverage.

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); settings (the time-segmented profiles in force); and basalDelivery (how the Omnipod 5 algorithm spent the window: minutes and percent in each state normal/suspend/max/limited, the longest suspend and longest max run, and the hour of day where suspend and max are most common. These are STATES, not insulin units; use get_basal_delivery for the timeline). All timestamps are plain wall clock time (see start/end parameter notes), not UTC.

Input 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.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.2.1

TDQS

A4.6/5.0
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 delivers: fixed-size aggregates regardless of span, cheap over months, tolerates very long windows, insulin derivation rules, the decisive best/worst ranking criteria, and that basalDelivery values are STATES not insulin units. It also flags the wall-clock-time caveat that governs interpretation of every timestamp.

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?

Front-loaded with the headline positioning, then clearly sectioned (TIP, insulin rule, ranking rule, Returns). It is long, and since no output schema exists the exhaustive field listing is justified, though some prose (ranking mechanics) is denser than strictly necessary.

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?

No output schema exists, so the description must describe return values — and it enumerates every returned group (reportRange, glucoseControl, glucoseExtremes, bestWorst, insulin, bolusArchitecture, carbs, settings, basalDelivery) with key sub-fields. An agent has everything needed to call 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 coverage is 100% and the schema already documents wall-clock semantics, defaults (OMNI_UNITS/LOWER/UPPER) and the enum. The description mostly defers ('see start/end parameter notes') and focuses on returns, adding little param meaning beyond the schema. Baseline 3 is appropriate.

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+resource (aggregated diabetes overview over a window) and positions itself explicitly as 'the single best starting point for any overview question.' It also names the sibling it is not — 'use get_basal_delivery for the timeline' — so an agent can disambiguate without opening 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 when-to-use conditions ('how was my control yesterday / over the last 3 weeks / last 6 months') and an explicit second use case: an uncapped wide call as an orientation/archive-discovery call. It also routes the agent away to get_basal_delivery for timelines.

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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