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Nightscout MCP Server

by adminpb

glucose_at_time

Retrieve the glucose value nearest a given time, with a customizable search window and surrounding readings for context. Answers questions like 'What was my glucose at 3 AM?'

Instructions

Get the glucose reading closest to a specific point in time. Returns the nearest reading within a configurable window, plus surrounding context (readings before and after). Use for: 'what was my glucose at 3 AM?', 'glucose when I woke up yesterday'.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
timeYesTarget time (ISO 8601 or 'YYYY-MM-DD HH:mm'). E.g., '2026-02-23T03:00:00' or '2026-02-23 15:30'
windowNoSearch window in minutes around target time (default 15, max 60)

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observedv0.4.0

TDQS

A4/5.0
Behavior3/5

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

With no annotations provided, the description carries the burden of behavioral disclosure. It discloses that the tool returns the nearest reading and surrounding context, which is useful. However, it does not mention behavior when no reading exists within the window, timezone handling, or whether the operation is read-only, leaving some behavioral uncertainty.

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 front-loaded. The first sentence states the core behavior, the second clarifies return content, and the examples illustrate real-world usage. Every sentence earns its place, with no fluff or redundancy.

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

Completeness4/5

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

For a relatively simple read tool with two documented parameters and no output schema, the description covers the essential return behavior (nearest reading plus before/after context) and gives practical query examples. It could be more complete by describing edge cases like no reading in range, but it is sufficient for correct invocation in common scenarios.

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 provides full descriptions for both parameters, including the time format and window default/max. The description's mention of a 'configurable window' loosely maps to the window parameter but adds no new semantic detail beyond the schema, so the baseline 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 clearly identifies the operation: getting the glucose reading closest to a specific point in time. It also adds distinctive details—'nearest reading within a configurable window' and 'surrounding context'—that set it apart from siblings like get_current_glucose and get_glucose_history, making the tool's purpose unambiguous.

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 provides concrete example queries ('what was my glucose at 3 AM?', 'glucose when I woke up yesterday') that clearly signal when this tool is appropriate. It does not explicitly mention alternatives or when not to use it, but the intended usage context is clear.

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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