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crawl_batch

Batch crawl multiple URLs with parallel extraction and automatic browser fallback for failed or incomplete pages.

Instructions

批量爬取多个网页(自动降级版)

适用于已经有一组明确 URL、需要并行抓取多个页面的场景。 默认对所有 URL 并行快速提取;只有失败、内容过短或 SPA 骨架页才进入浏览器 fallback。 若只有一个 URL,请使用 crawl_single;若只有入口页并希望发现站内链接,请使用 crawl_site。

参数组合:

  • prefer_fast=True:静态页走快速路径,失败项才走浏览器 fallback。

  • prefer_fast=False:所有 URL 都直接走浏览器,不返回 fast/fallback 方法标记。

  • min_content_length 只影响快速提取是否被认为足够完整。

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
urlsYesURL 列表
concurrentNo网页爬取并发数(默认:3)
llm_configNo[实验性] LLM 后处理配置(可选),需设置 CRAWL_MCP_API_KEY 才生效
prefer_fastNo是否优先尝试快速提取(默认 True)
llm_concurrentNoLLM 处理并发数(默认:3)
min_content_lengthNo快速提取最小内容长度阈值(默认 200 字符)

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior4/5

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

Without annotations, the description explains the key behavioral nuance: default parallel fast extraction with browser fallback on failure, too-short content, or SPA skeleton pages. It also discloses that setting prefer_fast=False changes the output by omitting fast/fallback method markers. This goes beyond a basic summary, though it doesn't mention potential side effects 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.

Conciseness5/5

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

The description is well-structured: it states the purpose, typical usage, alternatives, and then parameter combinations. It is concise, using bullet points for clarity, with no redundant information.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's complexity, the description covers the main use case, fallback behavior, and key parameter effects. An output schema exists, so return values need not be described. It does not detail concurrency or LLM config semantics, but those are documented in the schema, making the overall description sufficient.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

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 value by explaining how prefer_fast and min_content_length interact (e.g., min_content_length only affects whether fast extraction is deemed sufficient, and prefer_fast=False disables the fast path entirely). This contextualizes parameters beyond the schema's basic descriptions.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states it performs batch crawling of multiple web pages with an automatic degradation strategy. It explicitly says it's for scenarios with a clear set of URLs needing parallel crawling, and distinguishes itself from crawl_single and crawl_site.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides explicit guidance: use crawl_single for a single URL, use crawl_site for an entry page with link discovery needs. This gives clear when-to-use and alternative conditions.

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

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