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

With no annotations, the description carries full behavioral burden. It usefully discloses a genuine behavioral trait — unregistered concepts are not passed as safe but reported as undetermined — which is non-obvious. However it says nothing about the output shape or the strictness of the grade-boundary comparison, leaving gaps for an annotation-free 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?

Three tight sentences, front-loaded with the core action and then two supporting clarifications. No filler, though the final sentence slightly overlaps purposes already implied by the 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?

For a 2-param tool with full schema coverage and no annotations, the description covers purpose, a usage scenario, and one edge-case behavior. It omits any indication of the return value (no output schema exists), which an agent would benefit from knowing before invoking it.

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% with only 2 documented params, so the baseline is 3. The description's gloss (auditing a 説明文/解答 against a specified grade's known concepts) mirrors the schema's own descriptions rather than adding new parameter-level meaning.

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?

Concrete verb (監査する/audit) plus a clearly bounded resource: whether an answer's explanation contains known concepts taught after a given grade. The scope sense is distinct from get_prerequisites or search_curriculum, though it doesn't explicitly separate itself from the similarly named classify_knowledge_scope.

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?

It gives a concrete usage context ('中2までで説明して' style constraint checks), so an agent knows this is the constraint-verification tool. It stops short of naming when-not-to-use or explicitly routing to a sibling like classify_knowledge_scope.

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 of behavioral disclosure. It discloses one behavioral trait (prioritizing the MEXT code table if available), but omits critical details such as whether the operation is read-only, how ambiguous cases are resolved, or what confidence or ranking the output contains.

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 two sentences: the first front-loads the purpose, and the second adds a relevant caveat about data source priority. It is compact with no wasted words, though the caveat could be integrated more tightly.

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 no output schema and no annotations, the description should at least sketch the return format or confidence behavior. It does not describe what the output looks like (e.g., a ranked list of scopes), nor does it explain how the limit parameter affects ambiguous cases, leaving a significant gap for an agent invoking the 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 description coverage is 100%, so the schema already fully documents both parameters (query and limit). The description adds no parameter-level meaning beyond what the schema provides, which is the expected baseline when schema coverage is high.

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 a specific verb (判定します) and resource (specified knowledge/term in Japanese school education), clarifying it determines the school stage/grade/subject. It does not explicitly differentiate from siblings like check_answer_scope, but the scope is clear enough for an agent to understand the core function.

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 mentions prioritizing the MEXT normalized curriculum code table when available, but this is a data-source preference rather than a when-to-use guideline. It offers no explicit guidance on when to choose this tool over alternatives such as check_answer_scope or search_curriculum.

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 behavioral burden. It discloses that the tool returns a load status and per-code-table counts, which is useful, but it does not mention read-only nature, permissions, side effects, or any other behavioral traits.

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, efficient sentence with no wasted words. It front-loads the core purpose and appends the return detail 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?

For a zero-parameter status tool with no output schema, the description adequately explains what is returned (load status and per-code-table counts). It could be slightly more complete by clarifying the output format, but it is sufficient for correct invocation.

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 takes zero parameters, so the baseline score of 4 applies. There are no parameter semantics to clarify beyond what the empty schema already indicates.

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 a specific verb (returns) and resource (whether MEXT normalized full data is loaded, with counts per code table). It is clear enough to distinguish from the sibling tools, which focus on curriculum scope and search, but it does not explicitly name or contrast with any sibling.

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, nor any context about prerequisites or timing. It only states what is returned, leaving usage entirely implied.

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 behavioral burden. It does reveal one non-obvious trait: the fallback to a bundled seed graph when the primary source is incomplete. But it omits return structure, error behavior for unknown topics, and any performance or data-freshness caveats.

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?

Two compact sentences: the core purpose is front-loaded and the fallback caveat follows. No redundant restatement of the tool name or schema fields.

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 two-parameter read tool with no output schema and no annotations, the description covers the purpose and a notable data-source nuance but leaves the shape of the returned graph and behavior on missing topics unspecified. Adequate but with clear 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 description coverage is 100%, so the two parameters (topic, depth) are already documented in the schema, including the depth range and default. The description adds no syntax, format, or example beyond what the schema provides, making the baseline 3 appropriate.

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 a specific verb and resource: it returns ('返します') prerequisite knowledge ('前提知識') for a named topic. The purpose is unambiguous and the sibling names (classify_knowledge_scope, search_curriculum, etc.) are functionally distinct, but the description does not explicitly differentiate itself from them.

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?

It discloses a data-source fallback condition – when the official code table lacks concept dependencies, the bundled seed graph is used supplementarily – which implies usage context. However, it never states when to prefer this tool over siblings like search_curriculum or classify_knowledge_scope, nor any prerequisites or 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?

No annotations are provided, so the description carries the full burden. The verb '返します' implies a read-only listing operation with no side effects, but it does not explicitly confirm read-only behavior, absence of mutations, or any auth/rate-limit characteristics. It is minimally adequate for a simple 0-parameter helper.

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 sentence with no wasted words, front-loading what is returned and the consuming tool. Appropriate for a simple 0-parameter listing tool.

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 zero parameters and no output schema, the description sufficiently explains what the tool returns and its relationship to check_answer_scope. It does not specify the return format (e.g., array of scope identifiers), which would be helpful but is not strictly required for this low-complexity 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 tool has zero parameters, so there are no parameter semantics to explain. Per the rubric, a 0-parameter tool receives a baseline of 4; the description adds no misleading parameter 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 states a specific verb ('返します' / returns) and resource ('教育範囲の上限一覧' / list of educational scope limits), and explicitly ties it to check_answer_scope. This distinguishes it from siblings such as classify_knowledge_scope and check_answer_scope itself.

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?

It implies usage by naming check_answer_scope as the consumer of the returned scopes, so an agent can infer this should be called to discover valid inputs. However, there is no explicit when-to-use, when-not-to-use, or ordering guidance relative to alternatives.

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 behavioral burden. It does disclose one non-obvious output trait — that official data includes 学習指導要領コード in results — but says nothing about result limits, ordering, or the read-only nature of the operation, which the agent must infer.

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?

Two tight sentences, front-loaded with the search scope and closed with the distinguishing output detail. No filler or repetition of structured fields.

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?

It covers what is searched and one output variation, but with no output schema and no annotations the description should say more about result shape, result count, and read-only behavior. Adequate but with clear gaps for a five-parameter 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?

With 80% schema description coverage, the schema already documents query, stage, domain and subject. The description restates those same dimensions without adding format, syntax, or matching-behavior detail, and never mentions the limit parameter. Baseline 3 is appropriate.

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 names a specific verb (検索 / search) and resource (教育範囲データ / curriculum scope data), then enumerates the filter dimensions (教科・学校段階・領域・キーワード). An agent can tell it apart from siblings like classify_knowledge_scope or get_prerequisites, though it never names an alternative explicitly.

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?

There is no statement of when to use this tool versus the sibling tools, nor any exclusions or prerequisites. Usage is only implied by the word 検索; the useful detail that filters alone work when the query is omitted lives in the schema, not the description.

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