tool_analyze_jd

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.