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SuperGlookoQuery

by sikcapri
OverviewSchemaSourceRelated ServersScoreDiscussionsAdmin
JavaScript
Local

Glucose readings for a window (filterable by band)

get_glucose
Read-only

Retrieve raw CGM glucose readings for a date range, filtered by band (low, high, target, or all) to isolate hypo/hyper excursions or view the full trace.

Instructions

Individual timestamped CGM readings for a window, optionally filtered to just the part of the range you care about.

The "band" option decides which readings come back: "low" (below the low boundary, i.e. hypos), "high" (above the high boundary), "target" (in range), or "all" (every reading, each tagged with its band). Use "low"/"high" to pull only excursions for a close look without dragging in thousands of normal readings; "all" gives the full trace.

This returns raw points, so it is capped to 21 days. For a wide chart use get_chart_series (downsampled); for aggregate stats use get_diabetes_summary or get_trend rather than computing over a raw array yourself.

Glucose values are in the configured unit; times are plain wall clock time (device-local), not UTC.

Returns: window, thresholdsUsed (lower, upper, unit), the band requested, count, and a readings array (time, value, velocity, plus band when band="all").

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.
bandNoOptional (default: "all"). Which readings to return. "low" = below the low boundary (hypo); "high" = above the high boundary (hyper); "target" = in range, between the boundaries inclusive; "all" = every reading, each tagged with its band.all
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 observedv0.1.0

TDQS

A4.6/5.0
Behavior5/5

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

Beyond the readOnlyHint annotation, the description discloses critical runtime behavior: the 21-day cap on raw points, the plain wall-clock time semantics (explicitly not UTC), and that values follow the configured unit. It also previews the return shape, which is especially valuable given there is 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.

Conciseness4/5

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

The description is longer than average but well structured: a one-line summary, then the band selector explanation, then usage boundaries and caveats, then the return format. Every section adds operational value, though a small amount of repetition with the schema's parameter descriptions keeps it from a perfect score.

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 tool with no output schemathor, the description provides a complete mental model: what the tool returns, the cap, timezone handling, unit behavior, and how it relates to sibling tools. An agent has enough information to call it correctly and interpret the response.

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?

The input schema already has 100% parameter description coverage, so the baseline is 3. The description does reinforce the meaning of 'band' and explains the value of filtering, but it largely restates what the schema already documents rather than adding new parameter-level semantics.

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 statement: 'Individual timestamped CGM readings for a window', clearly identifying the resource and the operation. It further distinguishes itself from sibling tools by noting it returns raw points rather than downsampled chart series or aggregate statistics.

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?

The description gives explicit usage direction: use 'low'/'high' to pull only excursions without thousands of normal readings, and 'all' for the full trace. It also names concrete alternatives—get_chart_series for wide charts)Skip until get_diabetes_summary or get_trend for aggregates—so an agent knows exactly when to choose another tool.

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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