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

No annotations are provided, so the description fully carries the behavioral burden. It discloses that the browser opens non-headless, that login must be manually completed, that cookies are persisted per domain, and that timeout still saves current state—covering the key behavioral side effects.

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 well-structured with a short overview followed by an Args section. It is slightly verbose in places, but every part contributes useful operational context, and the key workflow information is 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?

Given the manual, interactive nature of this tool, the description covers the full call flow: why to invoke it, what the user will see, how the state is saved, and what happens after timeout. The existing output schema covers return values, so no additional explanation is needed.

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%, but the description fully documents both parameters: url specifies any page URL of the target site with login state keyed by domain, and timeout sets the manual-login waiting period with a defined timeout behavior. This adds substantial meaning beyond the bare 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 opens with a specific verb and resource: opening a GUI browser for manual login and persisting the site's login state to disk. It also explicitly ties itself to the web_fetch login_required=true workflow, which clearly distinguishes it from the sibling fetch 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 explicitly states when to use this tool: when web_fetch reports login_required=true for a site. It also explains the follow-up behavior (web_fetch will automatically carry the saved login state afterward), giving the agent clear situational guidance.

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 and does add useful behavior: concurrency, full processing chain per URL, per-URL token limits, and a batch default of 20. It does not cover failure handling, rate limits, or clarify what '完整分级链路' actually entails.

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-loaded with the core behavior, and uses a clear argument list with no filler. Each sentence contributes meaningful 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?

For a three-parameter tool with an output schema present, the description covers the essentials: target URLs, per-URL token budget, link policy, and result granularity. It is incomplete in that it omits error semantics, behavior when the batch limit is exceeded, and the precise meaning of the full processing chain.

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%, but the description compensates well by defining all three parameters: urls with a maximum/default count, max_tokens as a per-URL budget, and link_policy as equivalent to web_fetch. It loses one point because 'batch_max_urls' is referenced but not fully explained and link_policy values are not expanded.

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: '并发抓取一批 URL' (concurrently fetch a batch of URLs) and notes that results are returned per URL. This is clear and the batch name distinguishes it from web_fetch, though it does not explicitly contrast the two 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 batch behavior implies use for multiple URLs versus web_fetch for a single URL, and link_policy is explicitly delegated to web_fetch semantics. However, it does not state when to prefer this tool over web_fetch or mention alternatives.

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 provided, the description carries the behavioral burden. It discloses that extraction uses the same pipeline as web_fetch (tiered anti-crawling, login state, compliance), that it returns field-level data rather than full-page text, and details field-type semantics such as count and list behavior. It does not cover error handling or rate limits, but it provides meaningful 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 description is front-loaded with purpose and usage, then uses a compact example and a concise field-type reference. It is somewhat long, but the length is justified because the schema parameter requires documentation beyond what the input schema provides. The structure is logical and easy to scan.

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 tool with only two parameters, an output schema, and no annotations, the description covers the essential usage, parameter construction, and behavioral context. It explains when to use it, how to define extraction rules, and what kind of result to expect. Nothing critical is missing 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. It defines url as the target complete URL and provides an extensive breakdown of the schema parameter, including a full JSON example and detailed semantics for text, attr, count, and list field types with list_key options. This more than covers the minimal input 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: it scrapes a webpage and extracts structured JSON by field schema. It explicitly contrasts with web_fetch ('directly returns field values, not a compact text'), so an agent can distinguish it from the main sibling.

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 gives a direct use-case signal ('适合需要数据而非整页正文的场景') and explains that the scraping chain is the same as web_fetch. It does not explicitly enumerate when not to use it or mention web_batch, but the context is clear enough for correct selection.

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 provided, the description carries the full burden and does a good job: it discloses anti-crawler interception detection, truthful reporting, and the truncation strategy of keeping head/tail while omitting the middle when max_tokens is exceeded. This goes beyond what the schema alone would tell an agent.

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-loaded with the main purpose, and uses a clear bulleted Args section. Every sentence adds useful information without redundancy or filler.

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-value shape does not need to be described. The description covers the operation, URL requirement, token budget behavior, link policy variants, and an important edge case (anti-crawling pages). It is slightly incomplete only in not addressing relationship to sibling tools such as web_batch and login.

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 fully compensates by explaining the URL protocol requirement, the max_tokens truncation behavior, and every link_policy option with its default and trade-off. This gives an agent actionable parameter-level semantics that the raw schema lacks.

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: it fetches a web page and returns a token-efficient compact body. It also signals a distinctive behavior (auto-detecting anti-crawling block pages), which helps distinguish it from generic fetch tools, though it does not explicitly contrast with its 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 description explains what the tool does and how parameters work, but gives no guidance on when to prefer web_fetch over web_batch or login, nor any conditions where it should not be used. Usage context is only implied by the tool name and the single-page nature of the description.

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