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list_labels

Read-onlyIdempotent

Fetch internal classification labels that organize Skilljar content for your team. Use this to review or manage the private catalogue structure, separate from public tags shown to learners.

Instructions

List labels - the organization's INTERNAL classification of its content.

    Labels are not shown to learners. They are how a team keeps its own catalogue
    straight, so a label is evidence about how the organization thinks, not about
    what a customer sees.

    For the public equivalent use `list_tags`, which carries a `slug` and appears in
    catalogue URLs. Describing a label as something learners can browse is wrong in
    a way that is hard to spot.

    `list_course_labels` shows which labels are on one course.

    Requires `CSA_SKILLJAR_V1_API_KEY`, a separate credential from the v2 client.
    

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
pageNo
page_sizeNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
noteYes
pageNo
rowsYes
totalNo
has_moreNo
next_pageNo
Install Server

TDQS

A4.6/5.0
Behavior5/5

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

Annotations already mark the operation read-only and idempotent; the description adds valuable context by defining the internal semantic scope and flagging that a separate CSA_SKILLJAR_V1_API_KEY credential is required. This goes beyond the structured annotations without contradicting them.

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 efficiently structured and front-loaded with the core definition. Every sentence earns its place: meaning, learner-visibility, alternatives, and credential requirements are each covered without redundant content.

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 low-complexity list operation with two optional pagination params, a read-only annotation, and an output schema present, the description covers all essential context: what labels are, how they differ from tags, the related sibling, and the credential requirement. No critical gap remains.

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

Parameters2/5

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

Schema description coverage is 0%, so the description should compensate for the two parameters, but it does not mention page or page_size. The param names are somewhat self-explantory, yet the description adds no explicit guidance about pagination defaults or usage.

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 states a specific verb and resource — 'List labels' — and immediately clarifies that labels are an organization's INTERNAL classification of content. It explicitly differentiates this tool from llist_tags and list_course_labels, so an agent can identify the right sibling without inspecting schemas.

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 gives explicit routing guidance: use list_tags for the public equivalent and list_course_labels for label-to-course associations. It also warns against describing labels as learner-browsable content, which helps prevent a common misuse.

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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