Liepin Jobs

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It only mentions the major requirement and does not state side effects, validation rules, or return behavior. For a mutation tool, this is a significant gap.

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, with no redundant or filler content. It front-loads the primary purpose. However, the ambiguous phrasing '高中和初中以上' slightly detracts from clarity, preventing a top 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?

Despite the rich schema, the description lacks necessary context for an agent to effectively use the tool. It does not explain when to add education experience, what the expected outcome is, or how to handle the required fields. The major requirement is mentioned but ambiguously worded, leaving the description incomplete for a tool with 7 parameters and no output schema.

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 parameters are already well-documented. The description adds a rule about major being required for high school and above, which supplements the schema's note that major is optional for high school and below. While helpful, this is a minor addition given the schema's completeness.

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 '添加教育经历' (add education experience), which clearly identifies the action and resource, distinguishing it from siblings like add-work-exp or add-project-exp. However, the phrase '高中和初中以上' introduces slight ambiguity about which education levels are covered, so it doesn't earn a perfect 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 explicit guidance is provided about when to use this tool versus alternatives. The description neither mentions when not to use it nor suggests using modify-edu-exp for updates. The name implies an add operation, but the description itself does not convey usage context.

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?

Annotations are empty, so the description carries the full burden of behavioral disclosure. It only says 'add job expectation' with no details about side effects, idempotency, permissions, or return values.

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 short phrase that is under-specified rather than appropriately concise. It does not earn its place by conveying useful information beyond the name.

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 10 parameters, no output schema, and no annotations, the one-line description fails to provide essential context about what a job expectation is, how fields relate, or what the tool actually does in practice.

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 input schema provides thorough descriptions for all 10 parameters (100% coverage), so the description adds no additional parameter meaning. 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 '添加求职期望' is essentially a Chinese translation of the tool name 'add-job-want', restating it without adding specificity. It does not distinguish this tool from siblings like add-edu-exp or modify-job-want.

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 usage context is provided. The description does not explain when to use this tool versus alternatives such as modify-job-want, nor does it mention any prerequisites or conditions.

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?

Annotations are empty, so the description must fully disclose behavioral traits. It merely states the action with no mention of side effects, permissions, write behavior, or outcome. This is equivalent to the 'process' example with zero behavioral disclosure.

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 only two Chinese words, which is minimal but severely under-specified. It reads as a tautology of the tool name rather than providing useful structure or context.

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 8 parameters, 3 required, no output schema, and no annotations, a one-phrase description is grossly inadequate. The agent gains no understanding of input requirements, expected behavior, or return values beyond the schema.

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 already describes all 8 parameters with 100% coverage, so the baseline is 3. The description adds no parameter-level context or additional meaning 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 '添加项目经历' explicitly states the action (add) and the resource (project experience), which clearly conveys the tool's purpose. It is specific enough to distinguish from tools like add-work-exp, though it does not 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?

No guidance is provided on when to use this tool versus alternatives such as add-work-exp or modify-project-exp. The description gives no context about prerequisites, typical use cases, 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?

Annotations are empty, so the description must carry the full burden of behavioral disclosure. It only states 'Add work experience', with no information about side effects, required permissions, side effects, or error handling. The agent gains no insight into the operational behavior of this mutating 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 extremely short at four characters, which is under-specification rather than conciseness. It lacks necessary elaboration to be useful, and its brevity does not serve the agent's need for understanding tool behavior. This is similar to the 'Process' example.

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 complex tool with 18 parameters and no output schema, this description is woefully incomplete. It fails to explain return values, use cases, or relationships with sibling tools, leaving the agent to rely solely on the schema with no high-level understanding of the tool's purpose or 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?

The input schema provides extensive descriptions for all 18 parameters, achieving 100% coverage. The tool description itself adds no parameter-specific details, but since the schema already documents each parameter thoroughly, the baseline score of 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 '添加工作经历' directly translates to the tool name 'add-work-exp', essentially restating it without adding any specific scope or context. It does not distinguish this tool from sibling tools like 'add-edu-exp' or 'add-project-exp'. This is a tautology rather than a meaningful description.

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 is provided on when to use this tool versus alternatives such as 'modify-work-exp' or 'add-edu-exp'. The description is a generic phrase with no mention of prerequisites, exclusions, or appropriate use cases, leaving the agent without directional context.

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 burden of disclosing behavior. It adds a key validation rule ('传入的更新字段不能为空'), but does not cover permissions, reversibility, error handling, or response format. The single constraint is useful but minimal for a mutation 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 a single, concise sentence with two clauses: the verb+resource and the key constraint. Every word earns its place, and it is front-loaded with the primary purpose.

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 8 parameters, no output schema, and no annotations. The description provides the core behavior and a notable constraint, but lacks details on usage scenarios, prerequisites, or expectations for the update fields. The schema handles parameter descriptions, yet the overall context feels incomplete for an update operation.

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%, and the description adds no extra parameter meaning beyond referencing the ID and update fields. It merely restates the concept already present in the schema, so a baseline score of 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 clearly states the tool modifies education experience ('修改教育经历') by education ID, with a specific verb and resource. It distinguishes from sibling tools like add-edu-exp and other modify-* tools by targeting education experience specifically.

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 updating existing education records by ID, which is clear context. However, it does not explicitly mention when not to use it (e.g., for adding new records) or name alternatives, so it falls short of a full 5.

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 disclosing behavior. It only states that update fields cannot be empty, but does not mention whether it is a partial update, what happens if the ID does not exist, permissions required, or return format. For a mutation tool, this is a significant lack of transparency, justifying a score of 2.

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 concise sentences, front-loaded with the primary purpose and immediately stating the key constraint. Every sentence contributes value with no wasted words, meeting the 5 criterion.

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 11 parameters but all are fully described in the schema, and the description gives the core usage. However, without annotations or an output schema, it lacks information about response behavior, error scenarios, and whether it performs partial or full replacement. It is minimally adequate but not rich enough for a 4, hence a 3.

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%, and all parameters are defined. The description adds a valuable cross-cutting constraint: '传入的更新字段不能为空' (the passed update fields cannot be empty), which goes beyond the schema's sole requirement of `id`. This extra semantic guidance raises the score above the baseline 3.

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 a specific verb and resource: '修改求职期望' (modify job expectation) and specifies the update mechanism '按求职期望id更新' (update by job expectation ID). This distinguishes it from add-job-want and other modify-* tools, meeting the criteria for 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?

The description provides clear context: it is for updating an existing job expectation by ID. It does not explicitly mention alternatives or exclusions, but the ID-based update clearly implies the prerequisite of having an existing record, and the sibling naming (add-job-want) makes the distinction evident. Thus it earns a 4.

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 burden. It discloses the update semantics and a key constraint ('传入的更新字段不能为空' - update fields cannot be empty), but does not mention permission requirements, error behavior, or whether unspecified fields are preserved. This is adequate but with notable gaps.

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 short sentences that front-load the primary purpose and immediately follow with the update-by-id mechanism and constraint. There is no redundant information, making it optimally 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?

Given the simple update operation, full parameter coverage in the schema, and absence of an output schema, the description suffices for an agent to understand the tool's core behavior. It could mention that existing fields not passed are preserved, but the '按...更新字段' phrasing implies partial update, so it is nearly 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?

The input schema covers all 9 parameters with descriptions (100% coverage), so the baseline is 3. The description adds value by clarifying that the update fields must not be empty, a constraint not evident from the schema. This extra semantic detail justifies a 4.

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 modifies project experience by ID and updates specific fields. The verb '修改' (modify) and resource '项目经历' (project experience) are explicit, distinguishing it from sibling tools like add-project-exp or modify-work-exp.

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 updating existing project experience records, but it does not explicitly say when to use this instead of add-project-exp or other modify tools. No exclusions or alternative tool references are provided, making the guidance implied rather than explicit.

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 disclosure burden. It only states that fields are updated, but fails to disclose side effects on unmentioned fields, authorization requirements, return value, error behavior, or whether the operation is reversible. As a mutation tool, this lacks critical behavioral detail.

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 short sentence that immediately conveys the action and target. There is no filler or redundancy, making it highly concise and appropriately front-loaded.

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 17 optional parameters and no output schema, yet the description does not mention response behavior, error conditions, or usage context. It lacks essential operational details needed to invoke the tool confidently, making it incomplete for its 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?

The input schema already provides descriptions for all 17 parameters, so the baseline is 3. The description's phrase 'update the passed-in fields' adds a partial-update semantic, but it is a global statement rather than per-parameter clarification, and it does not significantly extend beyond the schema's existing 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 uses the explicit verb '修改' (modify) and clearly identifies the target resource as '简历的【基本信息】数据' (resume's basic information data), distinguishing it from sibling tools that modify other resume sections. It also conveys the partial-update behavior ('更新传入的字段数据'). However, it could more explicitly state its scope relative to other modify-* tools.

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 like modify-edu-exp or modify-work-exp. There is no mention of exclusions, prerequisites, or scenarios where this tool is preferred, leaving the usage context 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?

With empty annotations, the description carries the full burden of behavioral disclosure. It only states 'modify' without explaining whether the operation overwrites the entire self-assessment, whether it is idempotent, or if there are side effects. This is minimal behavioral information.

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 extremely short, essentially restating the tool name in Chinese. While concise, it is under-specified and fails to provide any meaningful information beyond the name, making it closer to a tautology than an effective description.

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 simplicity of the tool (one string parameter, no output schema), the description still lacks essential context such as usage scenarios, behavior details, or interactions with other resume components. It does not adequately explain when or how to use the tool in a real workflow.

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 input schema has 100% coverage with the parameter 'selfAssess' described as '自我评价内容'. The tool description adds no parameter-specific information beyond the schema, so a baseline score of 3 is appropriate since the schema already documents the parameter well.

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 '修改自我评价' clearly states the action (modify) and the resource (self-assessment). It distinguishes from sibling tools like modify-edu-exp and modify-work-exp by targeting a unique resume section, making the purpose 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 no guidance on when to use this tool versus alternatives. It does not mention any prerequisites, conditions, or situations where this tool should be preferred over sibling modify 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?

With no annotations, the description carries the burden. It adds a key behavioral constraint: '传入的更新字段不能为空' (passed update fields cannot be empty), which informs the caller that at least one field besides workId must be provided. However, it does not disclose other behavioral traits like partial update semantics, error handling, or permissions, leaving gaps typical of mutation tools.

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 brief sentences that front-load the purpose and follow with the critical usage constraint. Every word contributes value, with no redundancy or irrelevant details.

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?

This is a complex mutation tool with 19 parameters and no output schema, yet the description provides only minimal context. It does not explain return values, error conditions, or whether the update is partial or full. The constraint about non-empty fields is useful but insufficient for a tool of this complexity, especially without 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?

The input schema has 100% coverage with detailed descriptions for all 19 parameters, so the schema already explains each parameter's meaning. The description adds no parameter-specific details beyond the general 'non-empty' constraint, which is already implied by the schema's optional fields but not explicitly stated. Thus the description does not significantly enhance parameter understanding beyond the schema baseline.

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 '修改工作经验' (modify work experience) and specifies '按工作经验id更新' (update by work experience ID). This distinguishes it from add-work-exp and other modification tools, making the action and target resource 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 implies usage for updating an existing work experience but does not explicitly mention alternatives or exclusions. Sibling tool names provide context, but the description itself offers no direct comparison to add-work-exp or other modify tools, so guidance is implicit rather than explicit.

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 burden. It discloses that it returns the raw content of the resume, providing some behavioral context. However, it doesn't detail the output format, potential limitations, or permissions, leaving gaps.

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 short sentence that directly states the tool's action and output. It is front-loaded and contains no fluff.

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 read-only tool with no parameters, the description is minimally viable but lacks details about the return structure or any caveats. Given no output schema, additional context would improve completeness.

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 input schema has zero parameters, so the description doesn't need to explain parameter semantics. The baseline of 4 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 uses the verb '获取' (get) with the resource 'my resume', clearly indicating a retrieval operation. This distinguishes it from sibling tools that modify or search.

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 explicit when-to-use guidance is provided, but the tool's function as the only retrieval tool among siblings makes its usage context obvious. However, it doesn't mention alternatives 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?

Annotations are empty, so the description carries the full burden of behavioral disclosure. It only states that it 'gets matching job lists' based on filters, but does not disclose behaviors like result sorting, pagination, filter combination logic (AND/OR), or whether all parameters are optional. No additional context is provided about authentication, rate limits, or default behavior when filters are absent.

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 sentence that front-loads the purpose and enumerates all parameters. It is concise and efficient, with no redundant or filler content. Every phrase contributes to understanding the tool's functionality.

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 has 9 optional parameters, no output schema, and no annotations, the description is insufficiently complete. It does not explain the return value structure, pagination, or how multiple filters are combined. The absence of any distinction from 'user-search-job' leaves a significant contextual gap for the agent to choose 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 100%, so baseline is 3. The description adds value by clarifying 'jobName' as a keyword (职位名称关键词), providing examples for 'compNature' (国企、外企、民营) and 'salaryKind' (月薪/年薪), and framing 'salaryCap'/'salaryFloor' as a salary range ('薪资上下限'). These details enrich the parameter semantics beyond the schema's minimal labels.

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 a matching job list based on specified parameters, using the verb '获取' (retrieve) and resource '匹配的职位列表' (matching job list). It lists the filter parameters, making the purpose specific. However, it does not distinguish this from the sibling tool 'user-search-job', which likely serves a similar 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 provides no explicit guidance on when to use this tool versus alternatives such as 'user-search-job'. It simply lists the filtering parameters without indicating contexts, prerequisites, or exclusions. Given the existence of a similarly named sibling, this lack of usage direction is a notable gap.

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?

Annotations are empty, so the description must disclose behavioral traits. It only states 'apply for a job' without mentioning side effects such as creating an application record, requiring authentication, or whether the action is reversible. The verb 'apply' implies a mutation but no details are given.

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, concise sentence that communicates the tool's purpose without unnecessary words or repetition. It is front-loaded with the main action and parameters.

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 with only two required parameters and no output schema, so the description is minimally viable. However, it lacks behavioral context such as prerequisites (e.g., having a resume) or expected outcome, which would be helpful given the absence of annotations. 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?

The schema already provides descriptions for both parameters (jobId and jobKind), covering 100% of the schema. The description merely restates these in prose ('specify job ID and job type') without adding further semantic detail, so it meets the baseline but does not exceed it.

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 action: applying for a job with specific job ID and job type. It uses a specific verb ('apply') and resource ('job'), distinguishing it from sibling tools like search-jobs or add-job-want, which serve different purposes.

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 when to use the tool: when a user wants to apply for a job by providing job ID and job type. It does not mention explicit alternatives or exclusions, but the context is clear enough for an agent to select this tool over search or resume-editing siblings.

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?

Annotations are empty, so the description carries the full burden for behavioral disclosure. It only says the tool 'gets a matching job list' and lists filters; it does not reveal pagination behavior, result ordering, authentication needs, or how filters combine (e.g., AND/OR). This is a significant gap for a search tool with a `page` parameter.

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 long run-on sentence that packs in all filter criteria. It is not excessively verbose, but it lacks structuring and reads as a list dump. It could be more concise and front-loaded by stating the core purpose first and then the filters.

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 10 optional parameters but no output schema and no annotations. The description only says a matching job list is returned, omitting any mention of pagination, sorting, default behavior, or result format. Given the complexity and the existence of a sibling `search-jobs` tool, the description is not complete enough for an agent to reliably select and use 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%, so the baseline is 3. The description adds slightly more meaning by calling `jobName` a '职位名称关键词' (job title keyword) and by grouping salary bounds as '薪资上下限', but it largely repeats what the schema already documents. It makes no major contribution 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 retrieves a matching job list based on several specific filter criteria (experience, education, company nature, location, salary, etc.), so it is not a tautology. However, it does not distinguish itself from the sibling tool `search-jobs`, which appears to have a similar generic purpose.

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 is provided on when to use `user-search-job` versus `search-jobs` or any other sibling. The description only explains what the tool does, not the context, prerequisites, or exclusions. This leaves the agent without clear decision criteria.

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