get_discover_labels

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

Annotations are entirely absent, so the description carries the full behavioral disclosure burden. The verb '조회' (retrieve) implies a read-only operation, and the description explicitly says it returns aggregated counts, giving some transparency. However, it does not disclose any other behavioral traits such as response format, pagination behavior beyond the parameter default, or resource intensity. For a simple read tool, this is adequate but not rich, hence a mid-level score.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description consists of two sentences with zero fluff. The first sentence states the core purpose directly, and the second provides a concrete use case. The most important information (what it does) is front-loaded, and every sentence earns its place.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a tool with one optional parameter and no output schema, the description covers the purpose and a relevant use case. It implicitly notes the aggregation nature and the parameter is documented in the schema. While it could add a note about edge cases (e.g., no labels found) or explicitly state that it returns aggregated counts per label, such details are not strictly required for an agent to call the tool correctly. Overall it is sufficiently complete for its complexity.

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

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

The input schema provides a description for the only parameter 'first' ('조회할 레이블 수 (기본 20)'), so schema coverage is 100%. The description itself adds no additional parameter semantics beyond the schema. Per the calibration rule, with high schema coverage the baseline is 3, and there is no extra value added.

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

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

The description states a specific verb ('조회' = retrieve), a specific resource (Discover labels/tags), and the exact output (managed/unmanaged asset counts aggregation). It clearly distinguishes from siblings like get_discover_interfaces or get_unmanaged_interfaces by focusing on labels and their aggregated counts, so an agent can identify this tool without ambiguity.

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

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

The description provides a clear usage context: 'when creating a Tanium management ratio dashboard'. This tells the agent when to use it, but it does not mention when not to use it or name any alternatives among the sibling tools. Since the context is explicit but exclusions are absent, it fits the 'clear context, no exclusions' level.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.