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

With no annotations provided, the description carries the full burden. It correctly suggests a read-only operation ('조회합니다') without mentioning side effects. For a simple parameterless tool, this is sufficient, though additional details like authentication or rate limits could improve transparency.

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: a single sentence stating the purpose followed by a list of example questions. Every sentence serves a purpose, and the structure is front-loaded with the main action. No wasted words.

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 parameterless tool with no output schema, the description covers the core functionality and the kind of information retrieved (name, status, business info, etc.). It does not detail the return format, but given the tool's simplicity, this is adequate. Slightly higher could be achieved by explicitly stating it returns a single object.

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 has no parameters (0 params, 100% coverage trivially). According to guidelines, 0 params gives a baseline of 4. The description does not need to add param info since none exist.

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 retrieves Kakao Moment ad account info (name, status, business info, balance shortage) and provides example user queries. It is specific and distinguishes from siblings like get_bizmoney (balance) or get_performance_report (performance).

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 gives example questions (e.g., 'How is the ad account status?', 'Any balance shortage?') that directly indicate when to use the tool. However, it does not explicitly mention when not to use it or provide alternatives, though the context of sibling tools implies differentiation.

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 must convey behavioral aspects. It states the outputs but does not mention whether the operation is read-only, any authentication requirements, or data freshness. The description is functional but lacks depth in disclosing non-obvious behaviors.

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 exceptionally concise: two lines summarizing the core value followed by a compact list of example queries. Every sentence and example is purposeful, with no redundancy 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?

Given the lack of output schema and parameters, the description adequately covers the tool's purpose and typical questions. It does not specify output format or units, but for a simple query tool, the provided information is sufficient for an agent to understand and invoke it correctly.

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 has zero parameters with 100% coverage, meaning no additional documentation is needed. According to guidance, 0 parameters warrants a baseline of 4. The description adds no parameter info, but none is required.

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 provides three specific outputs: bizmoney balance, recent 7-day consumption trend, and remaining days estimate based on current pace. This distinguishes it from sibling tools like get_performance_report or get_today_status, which focus on different aspects of ad account data.

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 includes a list of example questions that map directly to common use cases, such as checking balance or estimating remaining days. While it does not explicitly state when not to use the tool, the examples provide strong contextual guidance for the AI agent.

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?

No annotations are provided; the description does not explicitly state whether the tool is read-only or has side effects. It implies a safe query operation but lacks explicit behavioral guarantees.

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 well-structured with bullet points and examples, but is relatively long. It could be slightly more concise without losing clarity.

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 tool's complexity (5 parameters, no output schema), the description covers parameter details, usage context, and output metrics comprehensively.

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 has 0% description coverage, but the description fully compensates by detailing each parameter with valid values, date formats, and optional breakdowns, including examples.

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 purpose: generating performance reports by period with key metrics (impressions, clicks, cost, conversions, CTR, CPC, ROAS). It distinguishes from siblings like get_today_status.

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 explicit usage examples mapping natural language queries to parameter values, and specifies when not to use it (e.g., for real-time pace, get_today_status is more suitable).

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?

No annotations are provided, so the description carries the burden. It implies a read-only query, but does not explicitly state whether it is destructive, required permissions, or rate limits. The description adds minimal behavioral context beyond the return values.

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 well-structured with a summary line, bulleted example questions, and a warning. It is concise but could be slightly trimmed; however, it remains efficient and front-loaded.

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 no parameters and no output schema, the description comprehensively explains the tool's return values (metrics listed) and usage context (questions and boundaries). It is complete for a simple status 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?

There are no parameters; schema coverage is 100%. The description correctly does not add parameter info, which is appropriate given zero parameters. Baseline for 0 params is 4.

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 that the tool returns today's ad account status with specific metrics (cumulative spend, hourly pace, etc.) and distinguishes itself from get_performance_report for past periods. The purpose is specific and 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 explicit example questions for when to use the tool and includes a warning with a direct reference to a sibling tool for past periods. This gives clear guidance on appropriate usage.

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?

No annotations are provided, so the description carries the full burden. It explains that with_details=True (default) adds bid/budget/period details, implying a read operation. However, it does not explicitly state that the tool is idempotent or non-destructive, though the context strongly suggests it.

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 well-structured with a summary line, usage examples, a warning, and parameter explanations. It is slightly long but every sentence adds value; 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?

Given two parameters, no output schema, and no annotations, the description covers purpose, parameters, and usage guidance adequately. It could be more explicit about the return format (e.g., array of objects), but the with_details parameter implies the structure. Overall fairly complete for a list 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 description coverage is 0%, but the description explains both parameters: campaign_id is 'close to required' for filtering by campaign, and with_details (default true) reinforces details per ad group. This adds significant meaning beyond the schema types and defaults.

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 lists ad groups within a specific campaign with details like bid, daily budget, execution period, and device. It provides example questions that illustrate its purpose, and distinguishes from sibling list_campaigns by focusing on ad groups.

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?

Explicit usage guidance is given with example questions (e.g., 'show ad groups of campaign X') and a clear warning: if only ad group ID is known, first use list_campaigns to find the campaign. This directly helps the agent decide when to use this tool.

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?

No annotations exist, so the description must disclose behavior. It mentions performance implications (with_details False when slow), but does not explicitly state that the operation is read-only or safe. The description adds some behavioral context but misses the opportunity to fully assure agents about safety.

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 front-loaded with a purpose statement and structured with example queries and an Args section. It is concise overall, though the example list could be slightly trimmed. Every sentence adds value.

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 list tool with no output schema, the description sufficiently explains the return fields (name, status, daily budget, type) and provides filtering and performance guidance. The sibling tools are distinct, so no cross-referencing is needed. The description is complete for its context.

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?

With 0% schema description coverage, the description fully compensates by explaining the status_filter values with concrete examples and defining the with_details boolean's effect and performance trade-off. This is far beyond what the schema provides.

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 starts with a clear Korean sentence stating the tool retrieves a full campaign list with specific fields (name, status, daily budget, type). The subsequent example queries reinforce the exact purpose. Although sibling tools are different, the description's clarity eliminates 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 example queries and explains when to use the status_filter and with_details parameters, including advice on turning off with_details for performance. However, it does not explicitly compare to sibling tools or state when not to use this tool, which would improve 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, the description discloses return fields (format, review status, preview, landing URL, title/description) and the effect of the with_details parameter. It does not mention pagination or rate limits, but adds sufficient transparency for a read-like tool.

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 front-loaded with the main purpose and well-structured with bullet-point examples and parameter explanations. It is appropriately sized for two parameters, though slightly verbose with example questions.

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 sibling tools and no output schema, the description covers query intent, return fields, and parameter usage. It lacks explicit mention of result format (e.g., list), but overall is sufficient for an agent to invoke correctly.

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 0%, but the description explains adgroup_id as nearly required for filtering to a specific ad group, and with_details as enriching per-creative detail. This adds meaning beyond the schema types and defaults.

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 specifies the tool lists creatives of a specific ad group, detailing return fields (format, review status, preview, landing URL, title/description). It distinguishes from siblings like get_performance_report by stating separate use for performance metrics.

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 explicit example questions for usage and warns that performance data should be retrieved via get_performance_report, offering a clear alternative. This shows when and when not to use the tool.

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