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

Discloses no date filter behavior and the comprehensive return data (basic info, property list, parties, location). Also mentions returning None if not found. Being a read-only lookup, no further behavioral details are critically missing, though authentication needs are not mentioned.

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?

Description is well-structured with separate sections for purpose, behavioral traits, args, and returns. Every sentence adds information without redundancy, and it is concise for the amount of detail.

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 lookup tool with 2 parameters and an output schema, the description covers purpose, usage guidance, parameter details, return behavior, and when it cannot find results. It is sufficiently complete given the tool's 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?

With 0% schema coverage, description fully explains both parameters: 'case_no' with a format example, 'court' with naming conventions for branch vs main court. This adds significant value beyond the schema.

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?

Description clearly states the tool's purpose: query court auction cases by case number. It distinguishes from sibling tool 'auction_search_by_case' by explicitly recommending this tool for specific case number lookups.

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 states preference over 'auction_search_by_case' and notes the absence of date filtering, implying it returns all statuses. However, it lacks explicit exclusions or alternative usage scenarios beyond the single sibling.

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 must cover behavioral traits. It only mentions returning None if no data, but fails to disclose read-only nature, permissions, side effects, or error handling. This is insufficient 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 brief and well-structured with Args and Returns sections. It is front-loaded with the purpose. Minor redundancy exists between the first sentence and the Args section, but overall it is concise.

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 an output schema present and moderate complexity, the description covers parameter purpose and return behavior (None if missing). However, it lacks information on potential errors, rate limits, or authentication requirements, leaving some gaps.

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 fully explains each parameter in Korean, including an example format for case_no and default behavior for item_no. This adds significant meaning beyond the schema names and types.

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 retrieves detailed information of a court auction item, including specific fields like appraised value and minimum price. This distinguishes it from sibling tools which are likely case search or captcha handling.

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 does not provide guidance on when to use this tool vs alternatives, nor does it mention prerequisites or when not to use it. Users are left to infer usage context from parameter descriptions alone.

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 carries the full burden of behavioral disclosure. It reveals key behaviors: only items with a sale date are returned, empty list means no ongoing items (not an error), and the return type is a list of item summaries. Though it does not mention authentication or read-only nature, those are implied and the description is sufficient for this simple 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 very concise, consisting of a few lines that efficiently convey purpose, usage guidance, parameter details, and return value. Every sentence adds value 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?

Given the tool's simplicity (2 parameters, simple list return) and the existence of an output schema, the description covers all necessary aspects: what it does, when to use it, parameters with examples, and return format. It is fully complete for an agent to understand and invoke the tool 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 description coverage is 0%, so the description must compensate. It does so by providing clear Korean descriptions for both parameters: case_no with example '2024타경12345' and court with example '서울중앙지방법원', along with optionality. This fully covers the meaning and format beyond the schema.

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 specifies the verb '검색한다' (search), the resource '법원경매 진행 중 매각물건' (court auction ongoing sale items), and the search key '사건번호' (case number). It explicitly distinguishes from the sibling tool auction_case_search by stating the condition under which that tool should be used instead.

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 states when to use the tool: to find ongoing items with a sale date set. It also explicitly states when not to use it (when there are no ongoing items, resulting in an empty list) and directs the agent to the alternative tool auction_case_search.

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 key behavior: it returns a CAPTCHA image and creates a session_id. It also notes that automatic recognition is not feasible. However, it does not mention if the operation is read-only or any side effects beyond session creation.

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 step label, clear sections for arguments and returns. A bit verbose with Korean but front-loads the purpose efficiently. Minor redundancy in repeating parameter names could be condensed.

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 output schema, the description adequately explains the return object (session_id, captcha_image_path). It covers the tool's role in the workflow and parameter requirements. Missing error cases or prerequisites, but overall sufficient for a tool with three required params.

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%, so the description must provide meaning. It does so with examples ('여수시법원', '25자10010'), constraints (exact match for court, required for party_name in ssgo), and explanations (case_no auto-parses case type). This adds critical value beyond the bare schema.

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 specific role: step 1 of a case search, returning a CAPTCHA image. It explicitly distinguishes from the sibling tool 'case_search_submit' which handles submission.

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 explains the sequential dependency with 'case_search_submit' and warns that CAPTCHA cannot be auto-recognized, guiding the user to read the image. It does not explicitly state when not to use, but the context implies it's only for initializing a search.

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 must carry the full burden. It describes input and output but does not disclose whether the operation is read-only, any potential side effects, or authentication requirements. Basic transparency is present but insufficient 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 with two sentences plus an args/returns section. It efficiently conveys the purpose, parameters, and return 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 the tool's low complexity (2 simple parameters, no output schema), the description fully covers the essential information: what it does, how to use it, and what it returns. No gaps are apparent.

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 adds crucial meaning: it explains that session_id comes from case_search_captcha and captcha_text is user-typed characters. This compensates well for the lack of schema descriptions.

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 completes a case search after CAPTCHA input, referencing the preceding tool (case_search_captcha) and specifying the return type. It distinguishes itself from siblings by being the second step in a two-phase process.

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 explicitly marks this as step 2, implying it should follow case_search_captcha. It provides clear context but does not explicitly state when not to use it or mention alternatives, which would be beneficial.

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