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Apuntar una nota

log_note
Idempotent

Apunta una nota libre en el diario (cómo se siente, algo fuera de lo normal…).

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": {
      "text": "<text>"
    },
    "name": "log_note"
  }
}

Input Schema

NameRequiredDescriptionDefault
textYes
timeNoWhen it happened, in the user's local time: 'HH:MM' (today), 'YYYY-MM-DDTHH:MM', or ISO 8601 with offset. Omit it for right now. Never in the future.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

B3.2/5.0
Behavior2/5

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

The annotations already declare write behavior (readOnlyHint=false), non-destructive, and idempotent, so the description's only additional behavioral signal is that the note is 'libre' (free-form). It says nothing about where the note is stored, whether it can later be edited or deleted via delete_entry, or what a repeat submission with identical text does despite idempotentHint=true.

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?

It is a single, front-loaded sentence with no redundant wording, and the parenthetical examples earn their place by clarifying the free-form nature of the note. The extreme brevity is appropriate for a two-parameter tool, though it leaves little room for any additional guidance.

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?

For a simple two-parameter, single-required-field write tool with no output schema, the description covers the core purpose but omits the lifecycle context an agent might need, such as whether notes are retrievable via get_entries or removable via delete_entry. It is adequate but leaves noticeable gaps for a mutation tool.

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 description coverage is 50%: the 'time' parameter is fully documented in the schema, while 'text' has no schema description. The description partially compensates by giving content examples ('cómo se siente, algo fuera de lo normal…') that indicate what belongs in 'text', but it adds no format, length, or language constraints for that field.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description uses a specific verb+resource combination, 'Apunta una nota libre en el diario', which clearly presents this as a free-form note write, distinct from the structured sibling loggers (log_glucose, log_meal, log_insulin). It provides concrete content examples ('cómo se siente, algo fuera de lo normal'), though it never names a sibling to differentiate from explicitly.

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 parenthetical examples imply when this tool applies, namely capturing free-text observations like feelings or unusual events, which suggests it is the free-text counterpart to the structured log_* tools. However, there is no explicit statement about when not to use it or which alternative to pick for structured data, so the guidance remains implicit.

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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