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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, covering the safety profile. The description adds the content scope (authentication, conventions, response codes, known issues) but does not disclose any additional behavioral aspects such as whether results are static, cached, or require specific permissions. With annotations present, the added content context justifies a 3.

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, concise sentence that front-loads the primary purpose. There is no redundant or filler content, and it efficiently conveys the full scope of what the tool returns.

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 read-only reference tool with an output schema, the description is complete. It lists the four categories of content returned, and the output schema covers the response structure. No additional prerequisites or caveats are needed, making this definition fully sufficient for an agent to call 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 tool has zero parameters, and the input schema is an empty object. The baseline for zero parameters is 4, and the description correctly implies no arguments are needed. It does not need to explain parameter formats or defaults since there are none.

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 returns authentication procedures, call conventions, response codes, and known issues. The verb 'returns' plus the specific content types make the purpose explicit, and the content differs from sibling tools like get_api_detail, which presumably provides API-specific details rather than conventions.

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?

No guidance is given about when to use this tool versus alternatives. The description implies it is for retrieving conventions, but it does not mention exclusions or contrast with siblings like get_api_detail or search_api. An agent would have to infer the appropriate usage context.

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 and idempotentHint, so no side-effect warning is required. The description adds substantial behavioral detail beyond annotations: REST code covers token issuance through response determination, realtime code uses websocket subscription, and order/exchange code includes a confirm=True safety gate before transmission.

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?

Four short Korean sentences front-load the core purpose and pack conditional behavior without repetition or filler. Each sentence adds a distinct piece of information: main action, REST/realtime variation, safety mechanism, and token-only mode.

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 one-parameter code generator with an output schema and safety annotations, the description covers the main generation modes and the confirm safeguard. The notable gap is that exact api_type values are not specified, and no pointer to sibling conventions or an enum is provided, so an agent may not reliably know which literal to pass.

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 only a bare string api_type with no description or enum, and schema coverage is 0%, so the description must compensate. It explains behavior for REST, realtime, order/exchange, and token, and even quotes api_type='token', but it never enumerates the exact accepted literal values or formats, leaving ambiguity about what string to pass for non-token modes.

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 precise verb-object statement: it creates Python code that calls the given api_type, then immediately distinguishes REST, realtime, and token modes. This clearly separates it from sibling tools like search_api and get_api_detail, which focus on API discovery rather than code generation.

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 clear context for how behavior changes by api_type (REST, realtime, order/exchange, token), which guides parameter choice. However, it never explicitly names sibling tools or states when to prefer this tool over list_categories, search_api, or get_api_detail, so alternative routing is left to inference.

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, idempotentHint=true, and destructiveHint=false, covering the safety profile. The description adds valuable context about the content of the returned details, which goes beyond the 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 a single, concise sentence that front-loads the purpose and lists the key contents. Every word earns its place 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 one-parameter read tool, the description is adequate: it states what is returned, and the output schema covers return values. Annotations handle safety. The only gap is parameter value guidance, but that is minor for a tool of this 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 sole parameter api_type has no description in the schema (0% coverage), and the description does not explain what valid values or format are expected. It only mentions 'api_type 의 상세' without providing any guidance on the parameter's domain.

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 ('returns the details of api_type') and specifies what details are included (path, parameters, response fields, cautions). It is a specific verb+resource that distinguishes it from siblings like list_categories or search_api.

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 a user needs detailed information about a specific api_type, but it does not explicitly contrast it with alternative tools or mention when not to use it. No exclusions or alternatives are provided.

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 idempotentHint=true, so the description is consistent and adds the specific return content (categories and counts). It does not disclose any further behavioral traits such as pagination or result limits, but the annotations cover the safety profile, so a mid score is appropriate.

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?

A single, direct sentence with no filler. The purpose is stated efficiently and the content is 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 the simplicity (no parameters, read-only, output schema exists), the description is minimally sufficient but lacks any usage context or differentiation from sibling tools. For a simple tool this is adequate, but not complete in terms of guiding correct selection.

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 the baseline is 4. The schema is empty and the description adds nothing about parameters, which is acceptable since there are none to explain.

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 the tool returns categories and API counts, which is a clear verb-resource pair. However, it does not differentiate itself from sibling tools like search_api or get_api_detail; the name itself provides most of the clarity.

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 no guidance on when to use this tool versus alternatives. It does not mention any context, exclusions, or relationships to sibling tools, leaving the agent to infer 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?

Annotations already mark the tool as read-only, idempotent, and non-destructive, and the description is consistent with this by saying it 'finds' APIs rather than performing actions. It adds useful behavioral context by describing natural-language querying, category narrowing, and auth-related search patterns, while avoiding any contradiction.

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: two dense sentences with no filler. The main purpose is front-loaded, examples come immediately, and the category and auth guidance are packed into the remaining text without 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 an output schema exists and annotations cover the safety profile, the description covers the essentials: how to search, what categories are valid, and how to target auth-related APIs. The only notable omission is explicit guidance on when to use sibling tools like get_api_detail or list_categories.

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%, so the description must compensate. It does so well: keyword is illustrated with realistic example queries, and category is enriched with the full list of supported values. It does not formally define keyword beyond examples, but the examples are sufficient for practical use.

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 a specific verb and resource: it finds APIs using natural language, with concrete examples like '삼성전자 현재가' and '매수'. It does not explicitly differentiate itself from sibling tools like list_categories or get_api_detail, so it falls short of a 5.

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 clear usage context: it explains that category can narrow results, lists the valid category values, and provides authentication-specific search keywords such as '토큰' and 'oauth'. It does not state when not to use this tool or mention alternative sibling tools, but the guidance is concrete and actionable.

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