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

No annotations are provided, so the description carries the full burden. It indicates a read-like lookup ('查') but does not disclose permissions, side effects, result limits, or other behavioral traits; output schema handles return shape but not operational behavior.

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 compact sentence that front-loads the action and includes examples. 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?

Output schema exists, so return values need not be described. But for a tool with no annotations and 0% schema coverage on its only parameter, the description leaves gaps: no sibling routing, no relation-type vocabulary, and no behavioral context.

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% and there is one required parameter. The description compensates partially by naming 'relation_type' and giving example values ('主将从现', '时态呼应'), but it does not define the valid relation-type set or format.

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?

States a clear verb-resource pair ('查知识点') with the filter ('按关系类型') and example relation values. However, it does not distinguish itself from sibling search_knowledge_points, 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?

No when-to-use, when-not-to-use, or alternative routing is provided. The relation-type filter is evident, but the agent is not told when this is preferable to search_knowledge_points.

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 burden. It usefully discloses the returned payload ('解释、例句、表格、溯源'), which adds value beyond the free-form object, and '获取' implies a read-only operation. It does not state permission requirements, behavior for a missing/invalid id, or failure modes.

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 front-loaded sentence with no wasted words; the resource and the trigger condition (id) come 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?

A one-parameter read tool with an output schema, so return-value shape is already covered by structured data and the description appropriately summarizes payload content. The main gap is the absence of any comparison to the search sibling.

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% for the single kp_id parameter, which is typed only as an integer. The description partially compensates by indicating the parameter is a knowledge-point id ('按 id 获取单个知识点'), but adds no format, range, or resolution details.

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?

States a specific verb+resource: retrieve a single knowledge point's full markdown by id. The word '单个' (single) implicitly contrasts with the sibling search_knowledge_points, but the description never names that alternative, so sibling differentiation is left to inference.

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 phrase '按 id 获取' implies the prerequisite that a kp_id must already be known, which is useful routing context. However, it gives no explicit when-to-use vs search_knowledge_points or any exclusion condition.

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 not state whether this is read-only (though the name implies it), what happens if an invalid number is given, whether output is cached, or how errors are surfaced. The format detail (GFM conversion) is helpful, but overall behavioral disclosure is thin for a tool with zero annotation coverage.

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 short sentences that are front-loaded with the core purpose, followed by a concrete example. No wasted words. It could be slightly more structured (e.g., separating behavior notes), but it is 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?

An output schema exists, so the description need not explain return values. However, with no annotations and a 0%-coverage parameter schema, the description should compensate more by clarifying read-only nature, error handling, or the relationship to list_lectures. As is, it is minimally complete for a simple read-by-id 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 0% and there is only one parameter ('number'). The description adds meaning by giving a concrete example (number=25 -> lecture 25) and implying the parameter is the lecture number. This is better than nothing but not comprehensive, so a baseline 3 is appropriate for a single-param tool where the schema itself is silent.

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?

States a specific verb (get/获取) and resource (complete markdown lecture notes), with the format detail that tables are converted to GFM. This distinguishes it from list_lectures (which presumably enumerates) and get_knowledge_point (different resource). The purpose is clear, though it doesn't explicitly contrast with siblings.

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 example ('number=25 returns 第二十五讲 动词时态3') implicitly signals when to use it: when you need the full markdown of a specific lecture by number. However, there is no explicit when-to-use vs. alternatives guidance, no mention of prerequisites (e.g., you must first know the lecture number via list_lectures), and no 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 behavioral burden. It discloses the returned fields and the 'all imported' scope, but does not mention ordering, permissions, pagination, or that the operation is read-only. For a simple zero-parameter list tool, this is minimally adequate.

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, compact sentence that front-loads the action and scope. Every element earns its place and there is 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?

The tool is simple, has an output schema covering return values, and zero parameters. The description states what is listed and what fields are included, making it nearly complete. Missing are explicit usage context and any safety/read-only note, but these are minor given the tool's simplicity.

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 description adds no parameter information, which is appropriate given there is nothing to document.

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 uses a specific verb ('列出' / list) and resource ('讲次' / lectures), and states the scope ('已导入的全部' / all imported) plus returned fields (lecture number, title, category). It clearly distinguishes itself from sibling tools by domain, but does not explicitly name or contrast with alternatives.

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?

Usage is implied by the tool's nature: use it to get a full list of imported lectures. However, there is no explicit guidance on when to prefer this over sibling tools like search_knowledge_points, nor are any exclusions or prerequisites stated.

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 the full burden. It adds meaningful behavioral context by declaring the implicit default value of category, and the read-only nature is inferable from '列出'. However, it says nothing about auth needs, result size, or pagination, which remains a gap for an unannotated 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 short lines, front-loaded with what the tool returns, then the default, then the narrowing option. Zero 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?

An output schema exists, so return values need not be explained, and both parameters are addressed with defaults and an example. Only the missing enumeration of accepted values and any usage boundaries keep it from being fully 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 compensate, and it does: it documents the default for category ('默认返回所有时态关键词') and gives a concrete usage example for tense ('tense="现在完成时"'). It still omits the full set of valid tense values, so it is not exhaustive.

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?

States a specific verb + resource ('列出标志词/关键词') plus the traceability angle ('可溯源到讲次'), which clarifies what the listed markers link back to. It is clearly distinct from siblings like get_knowledge_point or list_lectures, though it never names an alternative to differentiate against.

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 the default behavior (returns all tense keywords with category="时态") and how to narrow results via tense, which implies usage. But it never states when to prefer this over search_knowledge_points or find_by_relation, nor any exclusion.

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. It does not disclose whether this is a safe read-only operation, whether it requires permissions, whether results are paginated, or how the search behaves (e.g., exact match vs full-text). The bare return format '知识点列表' is thin behavioral context.

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 response is front-loaded with the core purpose, followed by a structured parameter list and a brief return summary. It is compact and every sentence serves to clarify invocation. Slightly verbose in enumerating category values, but that is necessary for an agent to pick valid values.

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 annotations and a 0% schema description coverage, the description steps in to cover all three parameters and the return shape, which is adequate. However, it does not describe pagination, ordering, or how to handle an empty result, leaving minor gaps for a search tool. Since an output schema exists, return values need not be fully explained, so the description's summary suffices.

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 explains each parameter: query is a keyword in Chinese or English with concrete examples ('现在完成时', 'since'), category limits to specific large classes (词法/句法/时态/语态/非谓语/综合复习), and limit controls the maximum number returned. This adds substantial meaning beyond the schema, though limit's type and default are only in 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 states a clear verb and resource: '按关键词检索语法知识点' (retrieve grammar knowledge points by keyword). It distinguishes from siblings like get_knowledge_point (singular retrieval) and list_lectures (a different resource), though it does not explicitly name them. The purpose is specific enough for an agent to know this is a search operation over knowledge points.

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?

Usage is implied by '按关键词检索' (search by keyword), but there is no explicit statement of when to use this tool versus alternatives such as get_knowledge_point or find_by_relation. The parameter descriptions hint at refining searches with category, but no exclusion conditions or preferred scenarios are given.

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 must carry the full behavioral burden, yet it says nothing about whether results are cached, whether the operation is read-only, its cost, or how the category distribution is structured. For a zero-parameter aggregation endpoint these omissions matter.

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 tight sentence that front-loads the verb and resource, with the enumerated metrics acting as scope. No filler, though it is terse to the point of under-specification.

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 existence of an output schema relieves the description of explaining return shapes, and there are no parameters to document. However, for a statistics endpoint over a complex knowledge base there is no mention of read-only behavior or when it should be preferred, leaving a gap given the absence of annotations.

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?

Parameter count is zero, so there are no parameter semantics to convey and the baseline is 4; the description correctly does not fabricate parameter discussion.

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?

States a specific action (return statistics) and resource (knowledge base) with enumerated sub-metrics (lecture/knowledge point/marker counts, distribution by category). This is clearer than a bare name but does not explicitly differentiate itself from siblings like list_lectures or list_markers, which also enumerate counts of the same entities.

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 on when to use this aggregation tool versus the sibling list_* tools that would return the underlying items. An agent must infer that this is the 'counts-only' option.

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