mcp-server-dexcom-health

export_data

Export glucose readings for external database storage. Retrieve clean, consistent data over specified time windows to build a long-term health history.

Instructions

Export glucose readings for persistence layer integration.

Returns clean, consistent data structure for storage in external databases.
Call periodically to build long-term data history.

Args:
    minutes: Time period to export (1-1440, default 1440 = 24h)
    start_minutes: Window start - minutes ago from now (e.g., 240 = 4 hours ago)
    end_minutes: Window end - minutes ago from now (e.g., 180 = 3 hours ago)
    format: Export format - "json" or "csv" (default "json")
    data: Optional external readings to format/export instead of fetching.

Examples:
    export_data(minutes=180)  # Export last 3 hours
    export_data(start_minutes=240, end_minutes=180)  # Export 4h to 3h ago

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": "export_data"
  }
}

Input Schema

Name	Required	Default
`data`	No
`format`	No	json
`minutes`	No
`end_minutes`	No
`start_minutes`	No

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.2.0Aug 10, 2026

TDQS

A4.5/5.0

Behavior4/5

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

Without annotations, the description carries the behavioral transparency burden. It discloses the return data structure ('clean, consistent data structure'), the optional data input behavior ('data: Optional external readings to format/export instead of fetching'), and detailed time-window semantics in the Args section. It does not mention side effects, authentication requirements, or error conditions, but the tool appears to be a read-like export with no destructive behavior.

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 front-loaded with a two-sentence summary, then a compact Args block with one-line parameter explanations, and ends with two concrete examples. No word is wasted, and the structure is clear.

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?

The description covers the tool's purpose, usage pattern, all five parameters with defaults, and provides examples. It is missing explicit conflict-resolution behavior (e.g., when both minutes and start/end_minutes are provided) and a precise output format specification, but given the absence of an output schema, it does a good job of informing the agent.

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

Parameters5/5

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

Schema coverage is 0%, so the description must fully explain parameters. It does so comprehensively: 'minutes: Time period to export (1-1440, default 1440 = 24h)', 'start_minutes...', 'end_minutes...', 'format...', 'data...'—and includes examples showing both minutes and start/end_minutes usage.

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 'Export glucose readings for persistence layer integration,' using a specific verb and resource. It further clarifies the export's purpose by stating it 'Returns clean, consistent data structure for storage in external databases' and 'Call periodically to build long-term data history,' distinguishing it from sibling read-focused tools.

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 provides clear context for use ('Call periodically to build long-term data history') and describes a distinct use case for persistence integration. However, it does not explicitly name alternatives or state when not to use this tool, so it stops short of full exclusion guidance.

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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