mcp-server-dexcom-health

analyze_time_blocks

Analyze glucose data by time block—overnight, morning, afternoon, evening—to pinpoint when high or low problems occur.

Instructions

Analyze glucose by time of day - find when problems happen.

Breaks down data into overnight (00-06), morning (06-12), 
afternoon (12-18), and evening (18-24).

Args:
    minutes: Time period if using Dexcom API (1-1440, default 1440 = 24h)
    low: Low threshold in mg/dL (default 70)
    high: High threshold in mg/dL (default 180)
    data: Optional external readings for persistence layer integration.

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": "analyze_time_blocks"
  }
}

Input Schema

Name	Required	Description	Default
`low`	No
`data`	No
`high`	No
`minutes`	No

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.2.0Aug 10, 2026

TDQS

A3.6/5.0

Behavior2/5

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

No annotations are provided, so the description bears full responsibility for disclosing behavior. It reveals that data is broken into four time blocks, but does not explain whether the tool fetches data from Dexcom automatically, how the `data` parameter interacts with `minutes`, or what side effects or return format are expected. The phrase 'persistence layer integration' is opaque.

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 compact and front-loaded: a purpose sentence, a clear list of time blocks, and a concise Args section. Every sentence contributes value, although 'find when problems happen' is slightly redundant with the first clause. The structure is 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?

With no output schema and no annotations, the description should explain what the tool returns and how it behaves. It does not mention the return format, nor does it clarify the relationship between `minutes` and `data`. However, it covers the core purpose and parameters adequately, making it minimally viable with clear gaps.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Since schema description coverage is 0%, the description compensates by explaining all four parameters: minutes (range 1-1440, default 1440), low/high thresholds (defaults 70/180 mg/dL), and data (optional external readings). This adds meaningful units and intent beyond the raw schema, though 'persistence layer integration' remains somewhat unclear.

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 states the tool's purpose: 'Analyze glucose by time of day - find when problems happen.' It further specifies time-block breakdowns (overnight, morning, afternoon, evening), which distinguishes it from sibling tools like get_statistics or detect_episodes by focusing on temporal patterns.

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?

The time-of-day focus implies when to use this tool, but there is no explicit guidance on when to choose it over alternatives like get_glucose_readings or get_statistics, nor any exclusions. The description provides context but leaves the decision to the agent's inference.

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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