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 add useful context beyond the name — the analysis relies on the user's 求职者画像 and produces a scoring/gap/cover-letter bundle rather than a bare analysis — but it says nothing about permissions, cost/latency of the LLM call, whether results are persisted, or rate limits.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The purpose sentence is front-loaded and dense with concrete deliverables, and the Args block is short. The Args block partially duplicates the input schema, but it is the only place the parameter meaning is documented given 0% schema coverage, so it earns its space.

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 spelled out (the description does so anyway, mildly redundantly). However, with zero annotations and no output-schema visibility into error/empty cases, the definition omits behavioral essentials such as profile prerequisites and whether the generated cover letter is stored, leaving the picture only minimally viable.

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, and it largely does: it explains jd_text as raw JD text or a requirements description, and characterizes provider as an optional LLM vendor with concrete examples (deepseek/aliyun/custom). It stops short of stating the default/fallback provider behavior, which would be needed for a 5.

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 and resource ('深度穿透解析目标企业招聘 JD') and enumerates concrete outputs (fit scoring, skill match points, gap list, cover-letter draft), so the agent knows exactly what the tool produces. It does not explicitly distinguish itself from overlapping siblings such as tool_one_click_tailor, which also appears to tailor application material, 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?

There is no when-to-use guidance, no prerequisites (e.g. whether a stored job-seeker profile must already exist), and no named alternatives. Given the sibling set contains tool_one_click_tailor and tool_generate_resume, which plausibly overlap with JD-driven tailoring, the absence of routing guidance is a real 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?

No annotations are provided, so the description must carry the full behavioral disclosure. It states it reads a local profile and outputs two formats, but does not disclose whether it overwrites existing files, what permissions are required, error behavior if the profile is missing, or any rate limits. This is a significant gap for a generation 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 front-loaded with the core action, followed by a compact Args section. Every sentence earns its place, with no redundant or filler text. Efficient and well-structured.

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. However, with no annotations, the description should provide more behavioral context, such as prerequisites (profile.yaml must exist), overwrite behavior, or error handling. It covers purpose and parameters adequately but leaves key operational details unspecified.

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 does so by documenting both parameters: 'template' with allowed values ('modern' or 'minimal') and 'output_name' with the note that no suffix is needed. This adds meaningful semantics beyond the schema, though it omits default values that are present 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?

States a specific verb and resource: '排版并编译输出高质量 PDF 与 Word (docx) 双格式简历' (typeset and compile high-quality PDF and Word dual-format resume). This clearly distinguishes it from siblings like tool_analyze_jd or tool_track_application, but it does not explicitly name or differentiate from any alternative, so it falls short of 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?

The description implies usage by saying it uses the local profile (config/profile.yaml), but provides no explicit when-to-use guidance, no conditions for selecting this tool over alternatives like tool_one_click_tailor, and no prerequisites or exclusions. This is no guidance beyond the basic purpose.

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 tells the agent the returned metric categories, which is useful context, and the read-oriented verb 获取 implies a safe retrieval operation. However, it does not disclose whether this requires authentication, whether data is scoped to the current user, or any other 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?

The definition is a single front-loaded sentence that states the action and resource immediately. The parenthetical metric list is long but serves the purpose of specifying the output scope, so it earns its place, though it slightly bloats the sentence.

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 no parameters and an output schema exists, so the description does not need to enumerate return values; it nonetheless lists them for scope. For a simple parameterless read tool, this is nearly complete, with the only gap being the absence of explicit usage guidance.

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 is 4. There is nothing for the description to clarify beyond confirming that the call requires no inputs.

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 (获取/get) and a precise resource (个人求职全流程转化漏斗统计/personal job-seeking conversion funnel statistics). No sibling tool provides analytics or funnel data, so it is clearly distinguishable from the resume, JD, project, tracking, and schedule 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 implies usage by naming the exact metrics returned, but it never states when an agent should call this tool versus other tracking tools. There is no direct sibling alternative for funnel analytics, so the lack of explicit guidance is less harmful, but the description does not actively route the agent.

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 does disclose a non-obvious behavioral trait: results include unclosed items from the past 3 days in addition to the future window, which the agent could not infer. It does not state read-only nature or sorting/limit behavior beyond that.

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, front-loaded sentences with no filler. The Args block is slightly redundant with the schema, but the size is appropriate for the tool's simplicity.

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 described. For a one-parameter read tool, the description covers the lookup window and the past-3-day inclusion, which 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?

Schema description coverage is 0%, so the description must compensate, and it does by explaining days_ahead as the future search window and its default of 7 days. It adds little about type or valid range, but the core semantics are conveyed.

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 (查询) and resource (笔试与面试待办日程提醒及倒计时), plus the time scope. It clearly distinguishes the retrieval of schedules from sibling write/analysis tools like tool_generate_resume or tool_track_application, though it never names a sibling 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?

The description implies when to use it (fetching upcoming to-do reminders) and supplies the default window, but gives no explicit when-not guidance or routing against alternatives. Usage is implied rather than 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 behavioral burden for a multi-step, side-effecting pipeline. It hints at a write (auto-registering the resume into the tracking board) but says nothing about permissions, files written to disk, cost/latency of the multi-step flow, failure modes, or what '防腐零污染' actually guarantees.

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 pipeline is front-loaded in a single arrow-separated line followed by an Args block, which is well ordered. However, decorative jargon such as '防腐零污染' consumes space without conveying actionable meaning, and the header mixes branding with 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?

An output schema exists, so return values need not be described, and the pipeline stages are enumerated. Still, for a complex mutation-heavy orchestrator with zero annotation coverage, the description omits partial-failure behavior, side effects beyond tracking, and prerequisites, leaving meaningful 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 0%, yet the description documents all three params and adds real meaning beyond the bare schema: jd_text accepts plain text or a file path, auto_track controls post-generation registration with a stated default, and provider is flagged as an optional LLM selector. Only provider lacks any semantic detail.

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 concrete composite verb chain (JD comparison → project augmentation → STAR reinforcement → dual-format resume → battle report → tracking registration), so an agent knows exactly what the tool delivers. It clearly reads as an orchestration superset of siblings like analyze_jd/generate_resume, but it never names or contrasts with them, so differentiation stays implicit.

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 '一键...全套交付流' framing implies the usage context (one call for the full pipeline), but there is no explicit when-to-use statement, no exclusions, and no mention of the granular sibling tools an agent could choose instead. Guidance is only inferable.

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 that it searches GitHub and uses an LLM to generate learning paths and resume STAR examples, but it omits operational details such as authentication needs, rate limits, latency, or cost.

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 compact, front-loads the core purpose, and uses an Args section to document parameters efficiently. Every part earns its place, though the title is absent and the parameter list could be slightly more structured.

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 an output schema present, the description need not explain return values. For a three-parameter recommendation tool with no annotations and zero schema coverage, it adequately covers purpose and parameters, but it lacks usage alternatives and deeper 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 description coverage is 0%, so the description must compensate. It documents all three parameters with useful meaning: skills are skill keywords with examples, language is the preferred primary language with default python, and provider is an optional LLM provider.

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: retrieves GitHub high-value open-source projects for skill gaps, then has an LLM generate a crash learning path and resume STAR examples. The purpose is clear, but it does not explicitly distinguish itself from sibling tools such as resume or JD analysis 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 phrase '针对技能短板' gives an implied when-to-use context, so the agent can infer this is for skill-gap project discovery. However, it does not state when not to use it or compare it to 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.

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

With no annotations, the description carries the full burden; it does disclose the create-vs-update branching and that notes accumulate as a per-change log. It does not state permissions, whether updates are reversible, or what happens to omitted fields, leaving meaningful behavioural gaps 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 purpose is front-loaded in one sentence, followed by a compact parameter list where each line adds distinct information. Slightly verbose in the repeated framing of the Args block, but there is no redundant prose to cut.

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 9-parameter dual-mode tool, the description covers both modes, every parameter, and example values; an output schema exists so return values need not be explained. The remaining omission is behavioural detail (permissions, partial-update semantics) rather than anything blocking 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?

Schema description coverage is 0%, so the inline Args block has to do all the work and largely does: it enumerates the accepted status values (wishlist/applied/assessment/interview_1/interview_2/hr_stage/offer/rejected/closed), explains the record_id branch, and supplies concrete format examples for time ('2026-09-28 14:00') and free-text fields.

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 opening sentence names a specific verb and resource (求职投递看板管理/登记投递记录) and immediately states the dual behaviour: create a new record or advance an existing one via record_id. This distinguishes it cleanly from siblings like tool_get_funnel_analytics and tool_get_upcoming_schedules, so an agent can pick it without opening a schema.

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 explicit mode-selection logic: pass record_id to update stage/schedule, omit it to create a new application, and lists concrete update cases (interview_1, scheduling, review notes). It does not name any alternative sibling (e.g. tool_get_upcoming_schedules for reading schedules back), which keeps it short of a 5.

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