google-keep-mcp

get_labels

Analyze label usage across notes and return sorted counts for label management. Optionally include archived or trashed notes.

Instructions

Analyze all labels across notes and return counts for label management.

Args:
    include_archived (bool, optional): Include archived notes in analysis (default: True)
    include_trashed (bool, optional): Include trashed notes in analysis (default: False)
    
Returns:
    str: JSON string containing label analysis with counts, sorted by usage

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": "get_labels"
  }
}

Input Schema

Name	Required	Description	Default
`include_trashed`	No
`include_archived`	No

Output Schema

Name	Required	Description	Default
`result`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedv1.2.0Jul 13, 2026

TDQS

A4.2/5.0

Behavior4/5

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

With no annotations provided, the description carries the full burden. It clearly states the tool is for analysis and returns counts, implying a read-only operation. The parameters for including archived/trashed notes are explained with defaults. However, it doesn't mention that the tool aggregates across all notes or whether it blocks or is heavy, but for a simple analysis tool this is acceptable.

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 extremely concise: one sentence for purpose, then a clear Args/Returns section. Every piece of information earns its place. No extraneous text.

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 simple analysis tool with two boolean parameters and a string return value, the description is complete. It explains what the tool does, the parameters, and the return format (JSON string with counts sorted by usage). The output schema exists, so return-value details are not needed.

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?

The description explains both parameters (include_archived and include_trashed) beyond what the schema provides (which only has titles and default values). It states their purpose and defaults, making the semantics clear. Schema description coverage is 0%, so the description fully compensates.

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 analyzes all labels across notes and returns counts for label management. This is a specific verb+resource combination that distinguishes it from sibling tools like search_by_label (which searches notes by label) or get_note_colors (which handles colors).

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 provides no guidance on when to use this tool versus its siblings. For example, it doesn't mention that this tool gives aggregate counts while search_by_label returns specific notes, or that get_note_colors is unrelated. This lack of context could lead to incorrect tool selection.

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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