HealthLedger MCP

Log a health metric

log_metric

Record a single health metric measurement with numeric value, optional unit, note, and timestamp. Use it to log time-series data like weight, blood pressure, or glucose.

Instructions

Record one quantitative reading (a point in a time series).

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": {
      "metric": "<metric>",
      "value": "<value>"
    },
    "name": "log_metric"
  }
}

Input Schema

Name	Required	Description
`note`	No	optional context ('after run', 'fasting', 'left arm').
`unit`	No	unit string, e.g. 'kg', 'mmHg', 'mg/dL', 'hours', 'bpm', '%', '°C'.
`user`	No	which person this belongs to; defaults to the primary user.
`value`	Yes	numeric value.
`metric`	Yes	snake_case name, e.g. 'weight_kg', 'systolic_bp', 'glucose_mgdl', 'sleep_hours', 'heart_rate', 'mood' (0-10), 'spo2', 'temperature_c'.
`timestamp`	No	when it was measured. ISO8601, 'YYYY-MM-DD', or 'now' (default).

Output Schema

Name	Required	Description	Default
No arguments

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.2.0Jul 9, 2026

TDQS

A3.6/5.0

Behavior3/5

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

Annotations already indicate a write operation (readOnlyHint=false) and non-idempotency (idempotentHint=false). The description adds the concept of time series but no additional behavioral details like side effects, required permissions, or rate limits. It is adequate but not enhanced beyond annotations.

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 a single, well-structured sentence that immediately conveys the tool's purpose. It is concise with no wasted words, meeting the standard for front-loaded clarity.

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?

Given the tool's simplicity (6 parameters, 2 required) and the presence of an output schema, the description covers the essential behavior. However, it could briefly mention that the metric is logged to a time series, but overall it is complete for a single-point write operation.

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 coverage is 100%, and the input schema provides adequate descriptions for each parameter. The tool description does not add extra semantic meaning beyond what the schema already offers, resulting in a baseline score.

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 'Record one quantitative reading (a point in a time series)' uses a specific verb ('Record') and resource ('quantitative reading'), clearly distinguishing it from sibling tools that add records like conditions or medications. It effectively conveys the core function.

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

Usage Guidelines2/5

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

The description does not provide any guidance on when to use this tool versus alternatives, nor does it mention when not to use it or list prerequisites. No context signals or exclusions are given.

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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