Skip to main content
Glama
AceDataCloud

mcp-webextrator

by AceDataCloud

webextrator_extract

Extract structured content from any web page by rendering it and returning product details, article content, or general page information, with optional LLM-enhanced semantic normalization.

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:
    By default, the extracted structured content inline. With mode="async", a JSON envelope with a `task_id` — poll `webextrator_get_task` until it reports `finished_at`, then read the extracted content from its `response`.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
urlYesThe URL of the web page to extract content from. Required.
modeNoProcessing mode. Defaults to API sync behavior. Pass 'async' to return immediately with a task_id to poll via webextrator_get_task.
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?

With no annotations, the description carries the full burden. It clearly explains the navigation/rendering/extraction flow, default synchronous return, async behavior with task_id polling, and LLM-enhanced normalization. It does not mention caching or error scenarios, but provides substantial behavioral context beyond a bare 'extracts content'.

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 compact, front-loaded with purpose, and uses bullet points for usage guidance. Each sentence contributes meaningful information—purpose, when-to-use, return behavior—without fluff or repetition.

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?

The description explains the primary use cases, return types, async workflow, and how to interact with sibling tools. While it does not cover every parameter's effect on behavior, the schema fills those gaps, and the description gives a solid mental model for this complex extraction tool.

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?

The schema descriptions cover 100% of the 15 parameters with detailed explanations including defaults and enums. The tool description adds little parameter-level detail beyond reiterating mode='async' behavior, which the schema already covers. Since schema coverage is complete, the baseline of 3 is appropriate.

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 opens with the specific verb 'Extract' and resource 'structured content from a web page', clearly distinguishing this from sibling tools like webextrator_render. It also enumerates the types of data extracted (product, article, general), reinforcing its unique role.

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 gives an explicit 'Use this when' list covering structured extraction needs, and even points to the correct sibling (webextrator_get_task) for polling async tasks. However, it does not explicitly state when NOT to use it (e.g., when rendering without extraction is needed), so it stops short of a 5.

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/AceDataCloud/WebExtratorMCP'

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