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

With no annotations provided, the description carries full burden for behavioral disclosure. It mentions the data source (Kakao Map) which adds some context, but doesn't describe what the analysis returns (format, structure), whether it's a read-only operation, potential rate limits, or authentication requirements. For a tool with 3 parameters and no annotations, this leaves significant behavioral gaps.

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 appropriately concise with three clear sentences. The first states the core function, the second lists outputs, and the third mentions data source. Each sentence adds value without redundancy. It could be slightly more front-loaded by combining purpose and outputs, but overall it's efficient.

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 3 parameters, 100% schema coverage, but no annotations and no output schema, the description is minimally adequate. It covers what the tool does and the data source, but doesn't explain what the analysis returns or behavioral aspects. Given the complexity of commercial area analysis, more context about output format and limitations would be helpful.

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 schema already documents all parameters thoroughly. The description doesn't add any parameter-specific information beyond what's in the schema descriptions. It mentions business type analysis generally, but doesn't provide additional context about parameter usage or constraints. Baseline 3 is appropriate when schema does the heavy lifting.

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: analyzing commercial areas with specific outputs (density by business type, saturation, characteristics). It uses specific verbs ('분석합니다', '제공합니다') and identifies the resource (commercial area at a specific location). However, it doesn't explicitly differentiate from sibling tools like 'find_competitors' or 'get_business_trends' which might have overlapping domains.

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 no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites, compare with sibling tools like 'find_competitors' (which might focus on competitors rather than area analysis), or specify scenarios where this analysis is most appropriate. The only contextual hint is the data source (Kakao Map).

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 carries full burden for behavioral disclosure. While it mentions analysis aspects (franchise ratio, market entry potential), it doesn't describe what the tool actually returns, whether it performs data aggregation, how analysis is conducted, or any limitations. For a tool with 4 parameters and no output schema, this leaves significant behavioral gaps.

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 with just two sentences that directly state the tool's purpose and analysis scope. There's no wasted text or redundancy. However, it could be more front-loaded by immediately stating it's for competitor analysis rather than starting with 'search and analyze surrounding competitors'.

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 4 parameters, no annotations, and no output schema, the description is insufficient. It doesn't explain what the analysis output looks like, how the franchise ratio and market entry potential are calculated or presented, or any limitations of the analysis. The agent needs more context to understand what to expect from this tool's results.

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 all parameters are documented in the schema. The description doesn't add any parameter-specific information beyond what's already in the schema descriptions. It mentions analysis aspects but doesn't connect them to specific parameters. The baseline of 3 is appropriate when the schema does the heavy lifting.

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: 'search and analyze surrounding competitors' with specific analysis aspects mentioned (franchise ratio, market entry potential). It uses specific verbs ('search', 'analyze') and identifies the resource ('competitors'). However, it doesn't explicitly differentiate from sibling tools like 'analyze_commercial_area' or 'get_business_trends' which might have overlapping functionality.

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 no guidance on when to use this tool versus the sibling tools. There's no mention of alternatives, prerequisites, or specific contexts where this tool is preferred over 'analyze_commercial_area' or 'get_business_trends'. The agent receives no help in selecting between these potentially related tools.

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 mentions analyzing trends but lacks behavioral details such as data sources, update frequency, rate limits, authentication requirements, or what the output looks like (e.g., structured data vs. summary). This is a significant gap for a tool with no annotations.

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 front-loaded, consisting of two sentences that directly state the tool's purpose. There is no wasted text, and it efficiently communicates the core function. However, it could be slightly more structured by explicitly listing key features.

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 complexity (3 parameters, no annotations, no output schema), the description is incomplete. It lacks details on behavioral traits, output format, and usage guidelines. For a tool that analyzes business trends, more context on data freshness, scope, and limitations is needed to be fully helpful.

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 description coverage is 100%, so the schema already documents all parameters (region, category, period) with descriptions and enums. The description adds no additional parameter semantics beyond what's in the schema, such as examples or constraints. Baseline 3 is appropriate when the schema does the heavy lifting.

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: analyzing recent startup trends and industry growth/decline status. It specifies the verb 'analyzes' and resource 'business trends/industries', making it understandable. However, it doesn't explicitly differentiate from sibling tools like 'analyze_commercial_area' or 'find_competitors', which might also involve business analysis.

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 no guidance on when to use this tool versus alternatives. It mentions what it does but doesn't specify contexts, prerequisites, or exclusions. For example, it doesn't clarify if this is for high-level trend analysis compared to more detailed tools like 'analyze_commercial_area' or 'find_competitors'.

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 carries the full burden of behavioral disclosure. It describes what the tool outputs (checklists, permits, costs, sequences) but lacks critical behavioral details such as whether it's a read-only operation, if it requires authentication, rate limits, or how data is sourced. For a tool with no annotation coverage, this is a significant gap in 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 and front-loaded, consisting of two efficient sentences that directly state the tool's offerings. There's no unnecessary information, and each sentence contributes to understanding the tool's purpose. However, it could be slightly more structured by explicitly separating outputs (e.g., checklists vs. costs).

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 moderate complexity (2 parameters, no output schema, no annotations), the description is minimally adequate. It covers what the tool provides but lacks details on behavioral aspects, output format, or error handling. Without annotations or an output schema, the description should do more to compensate, but it meets a basic threshold for a read-oriented 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?

The input schema has 100% description coverage, clearly documenting both parameters ('business_type' and 'region') with examples. The description adds no additional parameter semantics beyond what's in the schema, such as explaining how 'region' affects results or providing more context on 'business_type' options. Baseline score of 3 is appropriate since the schema does the heavy lifting.

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: providing industry-specific startup checklists, required permits, estimated costs, and preparation sequences. It uses specific verbs ('안내합니다', '제공합니다') and identifies the resource ('창업 체크리스트와 필요 인허가'). However, it doesn't explicitly differentiate from sibling tools like 'recommend_policy_funds' which might also provide financial guidance, leaving room for 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 offers no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites, exclusions, or compare with sibling tools such as 'analyze_commercial_area' or 'find_competitors', which might be relevant for startup planning. Usage is implied through the description's content but not explicitly stated.

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 carries full burden for behavioral disclosure. It mentions the types of recommendations (grants, loans, mentoring) but doesn't disclose critical behavioral traits like whether this is a read-only operation, if it requires authentication, rate limits, data freshness, or what the output format looks like. The description is functional but lacks operational 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 appropriately concise with two clear sentences that directly state the tool's function. It's front-loaded with the main purpose and follows with examples of what types of support are included. There's no wasted language, though it could potentially be structured to separate core function from examples.

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 moderate complexity (5 parameters, no output schema, no annotations), the description provides adequate but incomplete context. It explains what the tool does but lacks information about behavioral characteristics, output format, and usage boundaries. With no output schema, the description should ideally mention what kind of data is returned, but it doesn't.

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 schema already documents all 5 parameters thoroughly with descriptions and enums. The description doesn't add any parameter-specific information beyond what's in the schema. According to scoring rules, when schema coverage is high (>80%), the baseline is 3 even with no param info in the description.

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: recommending government/local government policy funds (grants, loans, mentoring programs) based on founder conditions. It uses specific verbs ('추천합니다', '안내합니다') and identifies the resource. However, it doesn't explicitly differentiate from sibling tools like 'get_startup_checklist' which might overlap in startup support context.

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 no guidance on when to use this tool versus alternatives. It doesn't mention sibling tools like 'analyze_commercial_area' or 'find_competitors', nor does it specify scenarios where this tool is preferred over others. The context is implied (when seeking funding recommendations) but lacks explicit usage boundaries.

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