Skip to main content
Glama

trigger_crawl

Manually trigger a crawl to fetch news from platforms like Zhihu or Weibo. Optionally save results locally or include URLs for detailed access.

Instructions

手动触发一次爬取任务(可选持久化)

Args: platforms: 指定平台ID列表,如 ['zhihu', 'weibo', 'douyin'] - 不指定时:使用 config.yaml 中配置的所有平台 - 支持的平台来自 config/config.yaml 的 platforms 配置 - 每个平台都有对应的name字段(如"知乎"、"微博"),方便AI识别 - 注意:失败的平台会在返回结果的 failed_platforms 字段中列出 save_to_local: 是否保存到本地 output 目录,默认 False include_url: 是否包含URL链接,默认False(节省token)

Returns: JSON格式的任务状态信息,包含: - platforms: 成功爬取的平台列表 - failed_platforms: 失败的平台列表(如有) - total_news: 爬取的新闻总数 - data: 新闻数据

Examples: - 临时爬取: trigger_crawl(platforms=['zhihu']) - 爬取并保存: trigger_crawl(platforms=['weibo'], save_to_local=True) - 使用默认平台: trigger_crawl() # 爬取config.yaml中配置的所有平台

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
platformsNo
include_urlNo
save_to_localNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior4/5

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

With no annotations, the description carries the full burden. It explains that failed platforms appear in failed_platforms, and describes the effects of each parameter. It does not disclose rate limits or destructive behavior, but it covers the main behavioral traits adequately.

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

Conciseness4/5

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

The description is well-structured with sections, args, returns, and examples. It is front-loaded with the core purpose. While slightly lengthy, each section earns its place without redundancy.

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

Completeness5/5

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

Given no annotations and an existing output schema, the description explains the return format and key behavioral aspects. It covers all necessary context for an agent to use the tool correctly, including default behavior and failure handling.

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

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 0%, so the description compensates fully. It explains the platforms parameter (list or null for all), default values, and side effects (failed_platforms). Examples show realistic usage, making parameter semantics clear.

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 the tool triggers a crawl task with optional persistence, using a specific verb ('trigger') and resource ('crawl task'). It distinguishes from sibling tools like analyze_data_insights or get_latest_news, which are analysis or retrieval tools, not crawl triggers.

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

Usage Guidelines4/5

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

The description provides clear usage context, including when to specify platforms and when to use defaults, along with examples. It does not explicitly mention when not to use this tool or list alternatives, but the context makes the usage pattern obvious.

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

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/lizouzt/TrendRadar'

If you have feedback or need assistance with the MCP directory API, please join our Discord server