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nightscout-mcp

nightscout-mcp

by vgmakeev
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Remote

analyze_monthly

Analyze continuous glucose monitor data by month, enabling yearly review of trends and time in range.

Instructions

Analyze glucose data broken down by month. Great for yearly reviews.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "year": "<year>"
    },
    "name": "analyze_monthly"
  }
}

Input Schema

NameRequiredDescriptionDefault
yearYesYear to analyze
tirGoalNoTIR goal percentage
toMonthNoEnding month (1-12)
fromMonthNoStarting month (1-12)

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.0

TDQS

A3.7/5.0
Behavior2/5

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

No annotations are provided, so the description carries the full burden. It only states the purpose and a use case but does not disclose whether the tool is read-only, what data it covers, or what the output looks like. This is a significant gap for a tool with no other behavioral hints.

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 short sentences with no filler. It succinctly states the purpose and a key use case, making it easy to scan.

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

Completeness3/5

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

The schema fully documents the parameters, and the description gives a clear purpose. However, since there is no output schema, the description should ideally hint at what the analysis produces. The current description leaves the output unspecified, which is a notable omission.

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 provides descriptions for all four parameters (year, tirGoal, toMonth, fromMonth), covering 100% of the schema. The description adds no additional parameter semantics beyond the monthly breakdown, 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 names a specific verb ('Analyze') and resource ('glucose data') with a clear scope ('by month'), and the phrase 'Great for yearly reviews' adds a use case. This distinguishes it from the sibling 'analyze' tool, which presumably does broader analysis.

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?

It explicitly suggests the tool is suitable for yearly reviews, giving a clear context for when to use it. However, it doesn't mention when not to use it or point to alternatives like 'analyze' or 'glucose_history'.

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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