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

With no annotations provided, the description carries full burden and does well. It discloses that the tool returns a list in the original registration order, specifies two error conditions (ValueError for empty clue_id, KeyError for unregistered clue_id), and indicates this is a retrieval operation rather than a mutation. It doesn't mention performance characteristics like rate limits or authentication needs, but covers the essential behavioral aspects.

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 clear sections (Args, Returns, Raises) and front-loads the core purpose. Each sentence earns its place by providing essential information. It could be slightly more concise in the Japanese portion, but overall it's efficiently organized with no wasted text.

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 (single parameter retrieval), no annotations, but with an output schema present, the description is complete. It covers purpose, parameter meaning, return behavior, error conditions, and prerequisites. The output schema handles return value documentation, so the description appropriately focuses on operational context rather than repeating schema information.

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 has 0% description coverage, so the description must fully compensate. It provides comprehensive parameter semantics: explains that clue_id identifies which set of candidates to retrieve, specifies it must be non-empty, and clarifies the prerequisite relationship with register_candidates. This adds significant value beyond the bare schema type information.

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 specific action ('取得する' - retrieve/get) and resource ('登録済みの候補語リスト' - registered candidate word list). It distinguishes from sibling tools by specifying it retrieves pre-registered candidates rather than registering them (register_candidates) or searching (search_consistent_sets). The purpose is precise 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 clear context about when to use this tool: when you need to retrieve previously registered candidates for a specific clue_id. It mentions the prerequisite that candidates must have been registered via 'register_candidates' first. However, it doesn't explicitly state when NOT to use this tool or name specific alternatives among the siblings, which prevents a perfect score.

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 and delivers comprehensive behavioral disclosure. It describes the tool's mutation behavior (adds candidates to existing state), idempotency (duplicates are ignored), validation logic (length matching, non-empty strings), error conditions, and return structure. It also explains state persistence ('既存の候補リストは保持したまま追記される' - existing candidate list is maintained and appended to).

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 and efficiently organized with clear sections (purpose statement, Args, Returns, Notes, Raises). Every sentence adds value: the opening statement defines the core operation, followed by organized technical details. No redundant or verbose content is present.

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 mutation complexity and lack of annotations, the description provides complete context. It covers purpose, usage prerequisites, parameter semantics, behavioral details (validation, idempotency, state changes), return values (though output schema exists, the description clarifies the dictionary structure), and error conditions. The presence of an output schema reduces the need to explain return types, but the description still adds valuable context about what 'registered' and 'rejected' contain.

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?

Despite 0% schema description coverage, the description fully compensates by providing detailed parameter semantics in the Args section. It explains clue_id must exist in setup-loaded definitions and gets trimmed, and candidates must be non-empty strings with length matching the clue's length. The description adds crucial validation logic not inferable from 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 the tool's purpose with specific verbs ('追加登録する' - add/register) and resources ('候補語' - candidate words) for a specific target ('指定したカギ' - specified clue). It distinguishes from siblings by focusing on registration rather than retrieval (get_candidates), rendering (render_solution), searching (search_consistent_sets), or setup (setup).

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 guidance: it states when to use (to add candidate words matching length to a clue), prerequisites (must call 'setup' first, clue_id must exist in loaded definitions), and exclusions (candidates must be non-empty strings, length must match clue's length). The 'Raises' section further clarifies error conditions that guide proper 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?

With no annotations provided, the description carries full burden and does so effectively. It discloses critical behavioral traits: it performs validation ('整合性チェックを通過した'), returns only consistent results ('交差が一致している場合のみ返される'), and raises specific errors for various failure modes (RuntimeError, ValueError). This covers safety, validation logic, and error conditions beyond basic input/output.

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 efficiently structured with a clear purpose statement, followed by organized sections for Args, Returns, and Raises. Each sentence adds value: the first defines the core action, and subsequent sections provide essential usage details without redundancy. It's appropriately sized for a tool with complex validation logic.

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 (validation, rendering, error handling), no annotations, and an output schema present, the description is complete. It covers purpose, prerequisites, parameter semantics, return format, and error conditions thoroughly. The output schema handles return structure, so the description appropriately focuses on behavioral context and constraints.

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 fully. It does so by explaining the parameter's purpose ('clue_id をキーとした解答文字列の辞書'), constraints ('setup 済みのすべてのカギに対して', '黒マス以外のセルを埋める語'), format requirements ('純粋なひらがなのみで構成されている必要がある'), and error cases related to it. This adds substantial meaning 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 specific action ('render_solution', '反映し', '返す') and resource ('盤面', '描画結果') with explicit scope ('全カギの解答', '整合性チェックを通過した'). It distinguishes from siblings like 'get_candidates' (retrieval) or 'setup' (initialization) by focusing on rendering validated solutions.

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 context by mentioning prerequisites ('setup 済みのすべてのカギに対して') and errors ('setup が未実行の場合'), and distinguishes from siblings by its focus on rendering validated assignments rather than searching or registering candidates. However, it lacks explicit when-not-to-use guidance or named alternatives for overlapping functions.

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 and does well by disclosing key behavioral traits: it explains the return format (list of dicts with clue_id and candidate word), error conditions (raises RuntimeError for uninitialized setup or no candidates, KeyError for invalid IDs), and edge cases (empty list for no consistent clues, enumerates multiple maximum solutions without duplicates). This covers critical operational aspects beyond basic 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?

The description is well-structured with clear sections (Args, Returns, Raises) and front-loaded purpose statement. It's appropriately sized, with each sentence adding value: the opening explains the core function, and subsequent parts detail inputs, outputs, and errors without redundancy. Minor room for improvement in flow keeps it from a perfect score.

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 (search tool with dependencies, error handling), no annotations, and an output schema present, the description is complete. It covers purpose, parameter semantics, return values, error conditions, and prerequisites. The output schema means return values don't need explanation, and the description adequately addresses all other contextual aspects for effective tool use.

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 adds meaningful semantics for the single parameter 'target_clue_ids': explains it's a list of clue IDs or None, clarifies that None or empty list targets all registered clues, and notes validation (raises KeyError for non-existent IDs). This goes beyond the schema's basic type definition, providing context and usage rules.

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: '探索する' (explore/search) for '割当て' (assignments) that satisfy '交差条件' (intersection conditions) from '登録済み候補' (registered candidates). It specifies the resource (candidate assignments) and verb (search/explore). However, it doesn't explicitly differentiate from sibling tools like 'get_candidates' or 'render_solution', which keeps it from a perfect score.

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 by mentioning prerequisites ('setup' must be executed, candidates must be registered) and dependencies on other tools. It also hints at alternatives by noting that 'target_clue_ids' can be None to target all clues. However, it lacks explicit guidance on when to use this tool versus siblings like 'get_candidates' or 'render_solution', 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 provided, the description carries full burden and does well at disclosing key behavioral traits. It explicitly states that calling this tool 'discards existing candidate lists and reinitializes the state' (destructive behavior), describes the return format (normalized cell matrix), and documents error conditions (ValueError for validation failures). It doesn't mention performance characteristics or rate limits, but covers the essential behavioral aspects for a setup/initialization 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 well-structured and appropriately sized. It starts with a clear purpose statement, then provides detailed parameter documentation in an Args section, followed by Returns and Raises sections, and ends with important Notes. Every sentence earns its place by providing essential information without redundancy. The information is front-loaded with the core purpose stated first.

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 this is a setup/initialization tool with 2 parameters, no annotations, but with output schema (returns list[list[str]]), the description is complete. It explains what the tool does, how to use parameters, what it returns, what errors can occur, and important behavioral notes about state reinitialization. The output schema handles return type documentation, so the description appropriately focuses on semantics and behavior.

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 0%, so the description must fully compensate - which it does excellently. It provides detailed semantic explanations for both parameters: grid_text format (row numbers, cell symbols, consistency requirements) and clue_text format (JSON Lines with specific fields, direction values, 1-based indexing). The description adds substantial meaning beyond what the bare schema provides, making parameter usage clear.

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 with specific verbs ('読み込み' - load, '初期化する' - initialize) and resources ('クロスワードの盤面とカギ定義' - crossword grid and clue definitions). It distinguishes from siblings by explaining this tool sets up/initializes the puzzle state while other tools like get_candidates, register_candidates, render_solution, and search_consistent_sets operate on that initialized state.

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 clear context about when to use this tool: to initialize the crossword puzzle state by loading grid and clue data. It mentions that calling this function discards existing candidate lists and reinitializes the state, which implies this should be used at the start of puzzle solving. However, it doesn't explicitly state when NOT to use it or name specific alternatives among the sibling tools.

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