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manage_labels

Create, rename, or remove project labels for priority, size, and state. Deletion is refused for labels still on cards to protect data.

Instructions

Create, rename or remove the labels a project uses for priority, size and state. A label still applied to cards is never deleted.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
colorsNoColour for labels being created or recoloured: {label name: colour}. Colours are Planka's own names, e.g. berry-red, lagoon-blue, sunny-grass. Unset ones cycle through the palette.
createNoLabel names to create, e.g. ['priority: high', 'size: L'].
renameNoMapping of existing label name -> new name.
project_idYesProject (tab) whose labels to manage.
delete_unusedNoLabel names to delete. Refused if the label is on any card.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior4/5

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

Annotations (readOnlyHint: false, destructiveHint: false) indicate this is a non-read, non-destructive operation. The description adds behavior beyond annotations by stating 'A label still applied to cards is never deleted', which prevents the agent from assuming deletion is unconditional. This addresses a key behavioral nuance that annotations alone do not cover.

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?

Two sentences, each earning its place: the first states the core purpose, the second clarifies a critical behavioral constraint. No wasted words, information is well prioritized.

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 5 parameters (100% schema coverage), rich annotations, and an output schema, the description fully addresses the use case. It explains the purpose, how parameters relate to categories, and a key side-effect (deletion refusal). The output schema presumably covers return values, so no need to mention them. Slight deduction for not mentioning that rename affects only name (not color/position) or that colors map to specific labels.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so baseline is 3. The description adds value by linking parameters to the label categories ('priority, size and state') and by explicitly noting deletion safety ('Refused if the label is on any card'), which the schema only implies for delete_unused. It also explains color semantics ('Unset ones cycle through the palette') beyond the schema's 'Colour for labels being created or recoloured'.

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 verb ('Create, rename or remove') and the resource ('labels a project uses'), distinguishing it from siblings like set_task_labels (which assigns labels to tasks). The scope is specific to label management at the project level.

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

Usage Guidelines4/5

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

The description implies when to use it (managing labels for priority, size, state) but does not explicitly state when not to use it or mention alternatives (e.g., set_task_labels for assigning labels to tasks). The context of 'project uses' is clear enough to infer usage boundaries.

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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