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

With no annotations, the description carries the full burden. It discloses key behaviors: inclusive counting, default forward direction for both sexes, the variant mode, and that it returns original text without generated interpretation. This is strong coverage. It could be improved by noting that omitting birth_time yields only year/month/day stars, but that detail is present in the schema.

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 is concise and densely packed, with three sentences covering purpose, output, counting/direction rules, and tool type. There is no redundant or filler content, and it is front-loaded with the primary function.

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?

Given that no output schema exists, the description should clearly explain return values and edge cases. It mentions the four periods and what is returned, but it omits the behavior when birth_time is not provided (hour star excluded, only year/month/day returned), which is an important nuance. This leaves a gap in understanding the tool's full behavior.

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?

Schema description coverage is 100%, so the baseline is 3. The description adds meaningful context beyond the schema by explaining the direction parameter's two modes, specifying that sex is required only for the variant, and clarifying the tool's data-only nature. This helps the agent understand how parameters interact.

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 clearly states the tool's function: it calculates the Dangsaju 12 stars from birth date/time and returns the branches, stars, and original explanation texts for the year, month, day, and hour periods. It also distinguishes itself from the sibling tool by emphasizing it is a data-only tool that does not generate interpretations, making 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.

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

The description provides context for use: it specifies the default forward direction and the male-forward/female-backward variant, and warns that it does not produce interpretations or advice, implying it is for raw data retrieval. However, it does not explicitly name the sibling tool or state when to prefer this over it, so it falls short of explicit exclusion guidance.

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

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

With no annotations provided, the description discloses key behavioral traits: it returns the full original text '그대로' (as-is), including symbolic, overall, and period-based sections. This gives the agent a clear expectation of the output's scope and nature, though edge cases like invalid inputs or both-parameter behavior are not addressed.

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 is a single, well-structured sentence that front-loads the main action and output, then adds context. Every phrase earns its place with no filler or repetition.

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?

Given the lack of an output schema, the description lists the output sections (상징, 총평, 시기별 해설) and the intended use case, providing sufficient context for an agent to decide when to invoke. Minor omissions such as the exact return type or error behavior prevent a perfect score, but it is complete for a simple lookup tool.

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?

Schema coverage is 100%, so the baseline is 3. The description adds meaningful semantics beyond the schema: it clarifies the 'or' relationship between jiji and star, provides example formats (Hangul and Hanja), and gives a specific example value, which helps correct usage.

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 uses a specific verb ('조회한다') and a specific resource ('별 원문 전체') to clearly state what the tool does. It also differentiates itself as a browsing/testing auxiliary tool, which implies a distinct role from the sibling dangsaju_reading.

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?

Explicitly identifies usage contexts: browsing, testing, and SKILL development. It does not list exclusions or directly contrast with the sibling tool, but the auxiliary-tool framing gives clear guidance on when this lookup should be used instead of a full reading.

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