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AceDataCloud

mcp-webextrator

by AceDataCloud

webextrator_extract

Extract structured content from a web page by providing a URL. Supports sync or async modes, cookies, custom headers, and optional LLM enrichment.

Instructions

Extract structured content from a web page using the WebExtrator API.

Navigates to the specified URL, renders the page, and extracts structured data
such as product details, article content, or general page information.

Use this when:
- You need to extract structured data from a web page
- You want product details, article content, or general page data
- You need LLM-enhanced semantic normalization of extracted content

Returns:
    JSON response containing the extracted structured content.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
urlYesThe URL of the web page to extract content from. Required.
modeNoProcessing mode. 'sync' (default) waits for the result; 'async' returns immediately with a task_id to poll via the Tasks API.
delayNoExtra delay in seconds after page load before extracting.
cookiesNoCookies to install before navigation. Each cookie is an object with at least 'name' and 'value', plus optional 'domain', 'path', 'expires', 'httpOnly', 'secure', 'sameSite'. Useful for authenticated pages.
headersNoExtra HTTP headers to include with the page request.
timeoutNoTotal timeout in seconds for page load. Default is 30.
enable_llmNoEnable LLM-based semantic normalization for richer structured output. Default is false.
user_agentNoOverride the User-Agent header for the page request.
wait_untilNoPage load wait condition before extracting. Options: 'load', 'domcontentloaded', 'networkidle', 'commit'. Default is 'networkidle'.
bypass_cacheNoSkip the Redis result cache for this request (still writes the fresh result back). Default is false.
callback_urlNoCallback URL for async processing. If provided, the task runs asynchronously and results are sent to this URL when complete.
expected_typeNoHint about expected page type. Options: 'product', 'article', 'general'. Helps the extractor optimize for the content structure.
block_resourcesNoResource types to block during page load to speed up rendering. Options: 'image', 'font', 'media', 'stylesheet', 'xhr', 'fetch'.
cache_ttl_secondsNoOverride the global cache TTL (seconds) for this entry. 0 means do not cache this response. Default is 3600.
wait_for_selectorNoCSS selector to wait for before extracting content.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior4/5

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

The description explains that the tool navigates, renders, and extracts data, mentions sync/async modes, LLM enhancement, and caching options (via parameter descriptions). It is transparent about core behavior but omits specifics on rate limits, error handling, or authentication needs.

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 concise at 8 lines, with a clear opening line, bulleted use cases, and a returns statement. It is well-structured and front-loaded, though the 'Returns' line is somewhat redundant with the schema.

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

Completeness3/5

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

Given the tool's complexity (15 parameters), the description covers purpose and key features but lacks details on error handling, prerequisites (e.g., network access), and the exact structure of the returned data. However, the output schema exists, which partially compensates.

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

Parameters3/5

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

All 15 parameters have descriptions in the input schema (100% coverage), so the description adds minimal extra meaning. It references expected_type and enable_llm in the overview but does not detail parameter usage beyond what the schema provides.

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

Purpose4/5

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

The description clearly states the tool extracts structured content from web pages via the WebExtrator API, with examples like product details and articles. However, it does not explicitly differentiate from sibling tools like webextrator_render, which might also access web pages.

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 'Use this when' section lists clear scenarios (extracting product details, articles, general data, or needing LLM normalization). However, it does not mention when not to use this tool or provide alternatives like webextrator_render for simpler rendering.

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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