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

The description reinforces the readOnlyHint annotation by explicitly stating 'never purchases'. It adds useful behavioral context about re-purchase rules (remaining round limit allows additional purchases) and mentions that remaining game count appears in 'round_remaining_games'. This goes beyond minimal annotation coverage.

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 well-structured: it opens with the core purpose (dry run), then explains the limit edge case. Every sentence earns its place and adds meaningful information.

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?

The tool has an output schema (not shown but present), so return values are covered elsewhere. The description explains the dry-run behavior, gate decision, limit handling, and remaining game field, making it sufficiently complete for a precheck tool given its simplicity and annotations.

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 already provides a full description for the only parameter 'games' ('number of games (1 game 1,000 won)'), so schema coverage is 100%. The description does not add extra parameter guidance, but that is not needed since the schema fully documents it.

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 it is a 'purchase pre-check (dry run)' that only provides gate decision and next step, never making an actual purchase. This distinguishes it from sibling tools like lotto_draw, which presumably performs the actual purchase.

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 clearly implies this tool is for pre-purchase validation: it 'only tells gate decision and next step' and 'never purchases'. This contrasts with lotto_draw, but it does not explicitly name alternatives or provide explicit when-not-to-use 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?

Annotations already declare readOnlyHint=true and openWorldHint=true, so the read-only nature is covered. The description adds useful context that results are 'public' and require no login, which goes beyond annotations. However, it does not disclose other behavioral traits such as handling of invalid round numbers or output format, which a dedicated output schema partially covers.

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 two short sentences, front-loaded with the core action, and every word earns its place. It is concise and well-structured, with no redundancy.

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 simple single-parameter read-only tool with an output schema, the description covers the essential purpose and access requirement. However, it does not differentiate from the sibling lotto_results, leaving potential confusion about which tool to use. This gap in tool selection context prevents a higher score.

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 schema provides a complete description for the sole parameter round_no ('회차 번호'), so schema coverage is 100%. The tool description does not add any additional semantic detail about how round_no affects the query, so it meets the baseline but adds no extra value.

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 action ('조회한다' = retrieves) and resource ('공개 추첨 결과' = public draw results), making the primary purpose obvious. However, it does not explicitly distinguish itself from the sibling tool lotto_results, which likely also deals with results, so it misses the full differentiator.

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 states '로그인이 필요 없다' (no login required), which implies it is for public access, but it does not explicitly indicate when to use this tool over alternatives like lotto_status or lotto_results. Usage is implied rather than clearly guided.

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?

The description adds significant behavioral context beyond the annotations: it explains that the rank is server-calculated, that the default 14-day window leads to '내역 없음' for older rounds, that the site caps queries at 3 months, and that the actual search window is returned in the `window` field. This discloses pitfalls and internal behavior that annotations 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.

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

The description is compact and front-loaded: it states the purpose first, then immediately addresses the critical nuance about `round_no` and the search window. Every sentence contributes unique information, with no redundant phrasing or filler.

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 read-only query tool with two optional parameters, an output schema, and annotations indicating safety, the description covers the essential user-facing behavior: the default window limitation, the workaround via `round_no`, and the site's hard limit. It also references the return field `window`, making the tool's behavior fully understandable for an agent.

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 only 50%, but the description compensates by explaining the semantic effect of `round_no` (expands the search window) and `days` (default 14, can be insufficient). It adds meaning beyond the schema's basic type and range descriptions, clarifying how parameters interact with the site's constraints.

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 opens with a specific verb and resource: '구매 내역과 당첨 결과를 조회한다' (queries purchase history and winning results). This clearly states the tool's function and distinguishes it from siblings like lotto_status, lotto_draw, and lotto_buy_precheck, which focus on different aspects.

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 clear context for when to use the tool and how to adjust parameters: passing `round_no` expands the search window to the site's 3-month limit, while the default 14 days may miss past rounds. However, it does not explicitly mention alternatives or exclusion criteria relative to sibling tools, so it falls short of a 5.

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?

Annotations already declare readOnlyHint=true, and the description's '조회한다' (inquire) is consistent. The description adds specific status fields being checked, but does not go beyond that. It doesn't describe return format or other behaviors, but with annotations covering safety and an output schema, this is adequate.

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, clear sentence that directly conveys the tool's purpose without any unnecessary words. It is perfectly concise and well-structured for the simple scope of the tool.

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?

With no parameters, readonly annotations, and an output schema present, the description is complete for this low-complexity tool. It clearly states what the tool returns information about, and there is no missing context for the agent to decide whether to invoke it.

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 tool has zero parameters, so no parameter semantics are needed. The baseline for zero parameters is 4, and the description appropriately adds no parameter information since there is none to add.

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 'queries' deposit balance, purchase limit, and validity of healthy purchase pledge. This is a specific verb and resource, and it distinguishes the tool from siblings like lotto_results, lotto_draw, and lotto_buy_precheck, which serve different purposes.

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 implies usage when one needs to check these status items, but it does not explicitly state when to use this tool over alternatives like lotto_buy_precheck. No exclusions or alternative references are given, so the guidance is only implied.

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