Wanhe Compliance — Chinese Content Compliance Checker

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

With no annotations, the description carries the full burden. It usefully discloses a key behavioral limit: the free tier performs only expression detection while full determination (explicit labeling + implicit metadata labeling) requires the cloud version. It says nothing about result format, confidence, or accuracy, so it is only partially transparent.

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 sentences, purpose front-loaded and the version limitation clearly appended. No filler and no repetition of the name. Slightly dense due to the regulatory citation but every clause carries information.

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?

No output schema and no annotations, so the description must fill the gap. It clarifies the free/cloud capability boundary, but never indicates what the detection result looks like (a verdict, a label suggestion, metadata?), leaving the agent guessing about the return value of a check 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?

Only one parameter, 'text', with 100% schema description coverage ('要判断的内容'). Per the rubric, a fully-covered schema sets the baseline at 3, and the description adds no syntax, format, or size guidance beyond 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?

States a specific verb and resource: detect AI-generated/synthetic expressions in content and flag labeling requirements under a named regulation. This is far more than a tautology. It does not, however, differentiate itself from the sibling check_content, so the agent cannot tell them apart from the description alone.

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 scopes what the free version will and won't do ('免费版只做表述检测；完整判定在云端版'), which implies when this tool is appropriate. But it gives no explicit when-to-use vs the sibling check_content and no exclusions or prerequisites beyond the version boundary.

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 and does well: it discloses determinism ('same input same output'), honest reporting behavior ('explicitly state when nothing found, no false reports'), and a per-session rate limit (30 calls) plus input size cap. These are meaningful operational traits beyond the schema.

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 action, output shape, coverage, rules, and limits are all present and reasonably front-loaded, with no filler sentences. It is dense but every clause carries information; the coverage enumeration is somewhat long but justified by scope definition.

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, the description compensates by describing the return format (per-item list of problem type / position / matched text / legal basis / revision suggestion), and covers limits and determinism. For a single-parameter read-only analysis tool this is nearly complete, missing only explicit sibling routing.

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?

There is a single parameter with 100% schema description coverage, so the schema already documents the '≤2000 characters' text input. The description restates the 2000-char cap but adds no syntax, format, or encoding detail beyond what the schema provides, so the baseline 3 applies.

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 — checking Chinese content for advertising-law compliance — and enumerates the exact rule categories covered (Article 9 absolute terms, Articles 17/18 medical claims, etc.), making the scope unmistakable. It does not explicitly name or contrast with the sibling check_ai_label, but the highly specific domain makes overlap implausible.

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 useful calling context (deterministic output, no false positives when nothing is found) and hard limits (2000 chars, 30 calls/session), which guide correct invocation. However, it never says when to choose this tool over the sibling check_ai_label or when-not to use it, leaving alternative selection to inference.

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