Nightscout MCP Server

carb_ratio_check

Evaluate meal boluses and post-meal glucose impact to compare real-world insulin-carb ratios against profile settings, identifying under- or over-bolusing.

Instructions

Analyze real-world carb ratios by evaluating meal boluses and their post-meal glucose impact. Compares actual ICR effectiveness vs profile settings. Identifies if you're under- or over-bolusing for meals.

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "carb_ratio_check"
  }
}

Input Schema

Name	Required	Description	Default
`days`	No	Days to analyze (default 7)
`target_rise_max`	No	Max acceptable post-meal rise in mg/dL (default 60)

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.4.0Sep 6, 2026

TDQS

A3.8/5.0

Behavior3/5

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

No annotations are provided, so the description carries the full behavioral burden. It does disclose meaningful behavior: it evaluates post-meal glucose impact, compares against profile settings, and classifies under-/over-bolusing. However, it does not state whether the operation is read-only, what data is required, or any limitations of the analysis.

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 two sentences with no wasted words. The primary action and object are front-loaded, and the second sentence adds valuable interpretive context about what the analysis identifies.

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 lightweight analysis tool with only two optional parameters and a fully documented schema, the description covers purpose, behavior, and expected interpretative output. It does not describe result formatting, but it does not have an output schema and still gives enough outcome context to guide invocation. It lacks sibling differentiation, but that gap is more about usage guidance than completeness.

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 does not add any parameter-specific meaning beyond the schema, but it also does not need to since days and target_rise_max are already documented in the input schema.

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 states a specific verb-resource pair: 'Analyze real-world carb ratios' and 'Compares actual ICR effectiveness vs profile settings.' It also clarifies the outcome ('Identifies if you're under- or over-bolusing for meals'), making the tool's role distinct from generic statistics or glucose history tools.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Usage context is implied rather than explicit: the description is clear that this tool is for evaluating meal boluses and carb ratios, but it does not state when to prefer it over siblings like analyze_meal or insulin_sensitivity_check, nor does it give exclusions or when-not-to-use guidance.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.