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

No annotations are provided, so the description carries full burden. It discloses that it extracts citations and optionally attaches context snippets. However, it does not mention any behavioral traits like whether it requires authentication, rate limits, or performance characteristics. Basic transparency is present but not rich.

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 relatively concise, using a single paragraph with clear bullet points for arguments. It could be slightly more structured, but every sentence adds information. 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?

Given no output schema and no annotations, the description adequately explains input and what is returned (citations with optional context). It lacks mention of return format or pagination, but for a simple extraction tool, it is reasonably complete.

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 add meaning. It does: it explains that case_id should follow the same format as get_interpretation, and includes that include_context controls whether a snippet of 80 characters is attached. This adds value beyond the bare schema 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 that the tool extracts all cited interpretation/case numbers from reasoning documents of Grand Justice interpretations. It specifies the trace direction as backward tracing (which prior judgments a given judgment cites), which distinguishes it from sibling tools like get_interpretation that retrieve a single interpretation.

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 you need to know which previous interpretations a specific case cites. It does not explicitly mention when not to use or name alternatives, but the context of sibling tools makes the use case clear. Slight lack of explicit guidance on exclusions.

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 behaviors: default fields from local cache (fast), reasoning/opinions have full-text and keyword modes, and reasoning_keyword overrides include_reasoning. It also mentions a 15000-character limit. However, it does not explicitly state whether reasoning/opinions require network access.

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 sections for default mode, reasoning/opinions, case_id format, and parameter details. While somewhat verbose, every sentence adds value. Minor redundancy could be trimmed, but overall 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?

Given the tool's complexity (5 parameters, no output schema), the description covers essential aspects: parameter behavior, caching, and case_id format. It lacks explicit output format details, but the tool's purpose implies returning the interpretation text. Generally sufficient.

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 description adds significant meaning beyond the input schema: it explains case_id formats (multiple accepted variants), the override behavior of reasoning_keyword and opinions_keyword, and the character limit for include_reasoning. With 0% schema coverage, this is essential and well-done.

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 full text of judicial interpretations or constitutional court decisions, specifying the range (釋字第1-813號 or 憲法法庭裁判) and caching behavior. It distinguishes from sibling tools like search_interpretations, which imply search 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 explains when to use the tool (retrieve specific known decisions) and provides formatting examples for case_id. However, it does not explicitly contrast with sibling tools like search_interpretations for when a search would be more appropriate, leaving some ambiguity.

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 full burden. It discloses the two query methods, preference for Open Data API with JID, and return structure. Lacks error handling or potential issues, but sufficient for a read-only fetch.

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 structured with Args and Returns, but the return list is somewhat verbose. It is front-loaded with the main purpose, and each part is useful.

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 provides the full return structure. It covers both query methods adequately for a single document fetch tool. No missing critical info.

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 provides detailed parameter descriptions with examples (jid format, url example), adding significant meaning beyond the schema 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 gets the full text of a single judgment (specific verb+resource). It distinguishes from siblings like search_judgments (searching) and get_citations (related items).

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 when to use the tool (to get a single judgment by JID or URL). It does not explicitly state when not to use it or mention alternatives, but the context is clear.

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 important behaviors: covering 11,700+ regulations, supporting fuzzy matching, and returning a dictionary with pcode or suggestions. It lacks details on error handling or rate limits but is adequate for a simple lookup 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 concise with two clear paragraphs, front-loading the purpose and providing Args/Returns sections. 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 simple one-parameter tool with no output schema, the description covers purpose, input, output format, and key behaviors like fuzzy matching and coverage, making it fully self-contained.

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 only provides the parameter name and type with 0% coverage. The description adds meaning by explaining law_name as a Chinese regulation name with examples (e.g., 民法, 勞基法), which goes 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 clearly states the tool's purpose: converting law names to pcode codes for the national law database. It is distinct from sibling tools like get_citations or get_interpretation, which serve different retrieval functions.

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 for converting law names to pcode, providing context of coverage and fuzzy matching. However, it does not explicitly state when to use this tool versus alternatives or when not to use it.

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 fully addresses behavioral aspects. It discloses automatic law_name-to-pcode conversion, the return structure (law, articles, source_url, optional history), and the functional scope. No destructive behavior is expected, and the transparency is sufficient.

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 clear purpose statement and bullet-style parameter explanations. It is slightly verbose but every part contributes to understanding. Concise enough for an AI agent to parse efficiently.

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 6 parameters, 0% schema coverage, and no output schema, the description covers all parameters and return structure. It could mention error handling or prerequisites (e.g., either law_name or pcode required), but it is largely complete for 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?

Schema description coverage is 0%, so the description compensates fully. It explains each parameter's purpose (e.g., law_name vs pcode, article_no for single article, from_no/to_no for range, include_history for history) with examples like '民法' and '247-1'. This adds significant meaning beyond the raw 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 it queries the national regulations database, specifying the types of queries (single article, range, full text). This distinguishes it from siblings like search_regulations, which likely searches across regulations rather than retrieving specific articles.

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 parameter usage (e.g., include_history for history queries) but does not explicitly guide when to use this tool versus alternatives like search_regulations or get_citations. Usage context is implied but lacks exclusions or alternative recommendations.

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 carries full burden. It discloses that keyword search covers '爭點 + 理由書', and explains parameter behaviors (e.g., year=0 applies to old system). However, it does not mention side effects, authentication, rate limits, or explicitly state read-only nature, which is acceptable for a search tool but leaves some ambiguity.

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 short introductory sentence followed by a parameter list. It is slightly verbose due to parameter explanations, but every sentence adds value. Could be more concise by integrating parameter notes into a single paragraph.

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 7 parameters and no output schema. The description explains parameters well but lacks details on return format (e.g., fields beyond case_id, pagination, sorting). It assumes output structure without specifying, leaving some gaps for an agent to infer.

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 has 0% parameter description coverage, so the description fully compensates by explaining each of the 7 parameters with conditions (e.g., 'include_old only effective when year=0'). This adds essential meaning beyond the schema's default values and titles.

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 enumerates (列舉) interpretations/constitutional court decisions and supports full-text keyword search across issues and reasoning. It specifies the resource (大法官解釋/憲法法庭裁判) and action (搜尋), distinguishing it from sibling 'get' tools like get_interpretation.

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 that results include 'case_id' for direct use with get_interpretation(), and details each parameter's effect (e.g., year filtering, max_results). It does not explicitly state when not to use or alternative tools, but the context of siblings implies 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 provided; description bears full burden. Discloses sorting by court hierarchy, returned fields (court, case_type, court_level), and that main_text is semi-structured. Does not cover auth needs or rate limits.

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?

Well-structured with sections and examples. Somewhat lengthy due to detailed main_text variants, but all information is relevant and adds value. Front-loaded with purpose and key usage.

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?

Comprehensive for a complex tool with 9 parameters. Covers sorting, caching, return format, parameter interactions, and advanced filtering. No output schema, but description details the return structure.

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 has no parameter descriptions (0% coverage). Description adds full semantics: keyword for full-text, court name, case_type domain, ROC year format, case_word/number for exact lookup, main_text for win/loss, max_results default and limit.

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 starts with '搜尋司法院裁判書系統' (Search the Judicial Yuan judgment system), clearly stating the verb and resource. It is distinct from sibling tools like get_judgment (single judgment fetch) and search_interpretations.

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?

Provides explicit guidance on when to use keyword vs case_word/case_number, with a concrete example. Describes advanced main_text usage for win/loss filtering. Does not explicitly mention when not to use or compare with sibling 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 provided, so description carries full burden. Explains sorting behavior, pagination, and exclude_abolished effect. Does not explicitly state read-only nature but implied by search functionality.

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?

Concise overall, front-loaded with purpose. Some redundancy in explaining exclude_abolished both in overview and in the parameter list. Still 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?

Covers purpose, scope, parameters, return values, sorting, pagination. No output schema, but return description is adequate. Complete for a search 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 has 0% description coverage; description adds meaning for all three parameters: keyword with examples, offset with default, exclude_abolished with behavior explanation. Significantly enhances 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?

Clearly states 'search regulation names by keyword', specifies scope (11,700+ regulations), and distinguishes from sibling tools that search other content like interpretations or judgments.

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?

Provides context on when to use (search in full regulation list), sorting by active regulations first, pagination of 50 per page. Does not explicitly state alternatives but sibling tool names imply different content types.

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