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clm_lean_card_command_lean_card_update

Destructive

Update a lean card's location, comment, dates, archived flag, volume, or transport unit; automatically recalculates its status and re-syncs site structure and team grouping counts.

Instructions

Updates an existing lean card's location (building/floor/laydown area), comment, dates, archived flag, volume, and estimated transport unit; recalculates its status; and queues background jobs to re-sync site-structure and team grouping counts using the old and new values.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
paramsYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observedv0.1.0

TDQS

A3.9/5.0
Behavior4/5

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

Beyond the annotations, the description discloses meaningful side effects: status recalculation and queued background jobs to re-sync site-structure and team grouping counts using old and new values. The destructiveHint and idempotentHint annotations are not contradicted; the description adds behavioral context the annotations do not convey.

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 dense sentence that front-loads the action and resource, then lists the affected fields and side effects in a logical order. Every part adds value, with no redundant or filler content.

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?

The description captures key side effects and the main field scope, but it leaves important operational details unstated, such as which parameter identifies the target lean card and how partial updates behave when most parameters are null/default. Since an output schema exists, return-value details are not needed, but the missing identifier guidance is a notable gap.

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?

With 0% schema description coverage, the description partially compensates by naming most updatable fields and grouping them semantically (location, dates). However, it omits ItemId, which is the likely identifier for selecting which lean card to update, and it does not clarify the behavior of omitted/null fields or whether all properties are optional.

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 uses a specific verb ('Updates'), identifies a specific resource ('existing lean card'), and enumerates the affected fields (location, comment, dates, archived flag, volume, transport unit). This clearly distinguishes it from sibling create/delete tools and makes its purpose unambiguous.

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 description implies the tool is for modifying an existing lean card, which is clear from the wording, but it does not explicitly contrast with sibling tools like clm_lean_card_command_lean_card_create or state when not to use it. No prerequisites or exclusions are provided, leaving usage guidance to inference.

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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