Cronometer MCP

remove_biometric

DestructiveIdempotent

Delete recorded biometric entries by ID, such as readings from a misbehaving scale, when a measurement never happened or is impossible to correct by editing.

Instructions

Delete recorded biometrics, such as a reading from a misbehaving scale.

Deleting is right when the measurement never happened or is impossible; edit_biometric is for a value that is merely wrong, since editing keeps the day's entry and its history.

Args: biometric_ids: List of biometric IDs to remove, from get_food_log. date: Date the entries belong to as YYYY-MM-DD (defaults to today).

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": {
      "biometric_ids": "<biometric_ids>"
    },
    "name": "remove_biometric"
  }
}

Input Schema

Name	Required	Description	Default
`date`	No
`biometric_ids`	Yes

Output Schema

Name	Required	Description	Default
`result`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedv2.1.0Nov 14, 2026

TDQS

A4.8/5.0

Behavior4/5

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

Annotations already mark this as destructive, non-read-only, and idempotent. The description adds useful semantic context beyond those flags: it clarifies that deletion is for invalid/cancelled measurements and that editing preserves the day's entry and history, implying deletion does not. It could be more explicit about permanence, but the destructive annotation covers the core risk.

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 compact and front-loaded with the core action and example, followed by decision guidance, then parameter details. Every sentence contributes useful information without repetition or filler.

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

Completeness5/5

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

For a two-parameter destructive tool with annotations and an output schema, the description is complete. It covers what is deleted, when to delete versus edit, where IDs come from, and date handling. No critical calling information is missing.

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 description coverage is 0%, so the description carries the full burden. It documents both parameters: biometric_ids lists the IDs to remove and identifies their source ('from get_food_log'), and date specifies the format YYYY-MM-DD and the default behavior (defaults to today). This adds meaning well beyond the raw 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 begins with a specific verb and resource: 'Delete recorded biometrics'. It gives a concrete example ('a reading from a misbehaving scale') and explicitly contrasts itself with edit_biometric, which clearly distinguishes this tool from its closest sibling.

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

Usage Guidelines5/5

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

The description explicitly states when deletion is appropriate ('when the measurement never happened or is impossible') and when editing should be used instead ('a value that is merely wrong'). This provides clear decision-making guidance and names the alternative tool.

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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