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WebScraping-AI MCP Server

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by webscraping-ai
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JavaScript
Remote

Ask a Question About a Page

webscraping_ai_question
Read-only

Scrape a web page and ask an LLM a single question about its content, returning a plain-text answer based only on that page.

Instructions

Scrape a web page and have an LLM answer a question about it, using only the page's own content. Returns the answer as plain text. If the page lacks the information or the question can't be answered from it, the tool returns an error result whose body.error starts with "Invalid page or question", and the credits are refunded. Costs 5 credits on top of the scrape's normal cost. Suited to a single fact or short answer; use webscraping_ai_fields for several named values at once, or webscraping_ai_text to read or reason over the whole page yourself. API docs: https://webscraping.ai/docs#ai-question

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
jsNoExecute on-page JavaScript using a headless browser (false by default).
urlYesURL of the target page.
proxyNoType of proxy: datacenter, residential, or stealth (datacenter by default). Use residential if the site restricts datacenter traffic, or stealth for the most heavily protected sites with advanced anti-bot detection. Residential and stealth requests cost more than datacenter — see the pricing page.datacenter
deviceNoType of device emulation.
countryNoCountry of the proxy to use (US by default).
timeoutNoMaximum web page retrieval time in ms (20000 by default, maximum is 30000).
questionYesQuestion or instructions to ask the LLM model about the target page.
wait_forNoCSS selector to wait for before returning the page content.
js_scriptNoCustom JavaScript code to execute on the target page.
js_timeoutNoMaximum JavaScript rendering time in ms (3000 by default).
custom_proxyNoYour own proxy URL in "http://user:password@host:port" format.
error_on_404NoReturn error on 404 HTTP status on the target page (false by default).
error_on_redirectNoReturn error on redirect on the target page (false by default).

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changedv1.2.2
    • addedInput schema / additionalProperties
      Added value: +false
  2. Changed1 schema field changedv1.0.5
    • changedInput schema / properties / country / enum
      Previous value: -[
-  "us",
-  "gb",
-  "de",
-  "it",
-  "fr",
-  "ca",
-  "es",
-  "ru",
-  "jp",
-  "kr",
-  "in"
-]New value: +[
+  "us",
+  "gb",
+  "de",
+  "it",
+  "fr",
+  "ca",
+  "es",
+  "ru",
+  "jp",
+  "kr",
+  "in",
+  "hk",
+  "tr"
+]
  3. Changed2 schema fields changedv1.0.4
    • changedInput schema / properties / proxy / description
      Previous value: -"Type of proxy, datacenter or residential (datacenter by default)."New value: +"Type of proxy: datacenter, residential, or stealth (datacenter by default). Use residential if the site restricts datacenter traffic, or stealth for the most heavily protected sites with advanced anti-bot detection. Residential and stealth requests cost more than datacenter — see the pricing page."
    • changedInput schema / properties / proxy / enum
      Previous value: -[
-  "datacenter",
-  "residential"
-]New value: +[
+  "datacenter",
+  "residential",
+  "stealth"
+]
  4. Addedv1.0.2

TDQS

A4.7/5.0
Behavior5/5

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

Goes well beyond the annotations by disclosing the exact error contract (body.error starts with 'Invalid page or question'), that credits are refunded on failure, and the specific cost model (5 credits on top of the scrape's normal cost). These are behavioral traits an agent cannot infer from readOnlyHint/openWorldHint alone.

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?

Front-loads what the tool does and its return type, then routing/error/cost details, then the docs link. Every sentence conveys distinct, actionable information with no 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?

Despite 13 parameters and no output schema, the description covers return format ('plain text'), the error shape and refund behavior, cost, and sibling routing. Nothing an agent needs to invoke it correctly is missing.

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?

Schema description coverage is 100%, so all 13 parameters are already documented in the schema; the description adds no per-parameter detail (e.g., js, proxy, or question syntax). Baseline 3 is appropriate when the schema carries the full parameter burden.

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?

States a specific verb chain (scrape a page, have an LLM answer a question using only the page's own content) and the return type (plain text). The scope constraint 'using only the page's own content' distinguishes it from tools that read or manipulate the page, letting an agent separate it from siblings without opening schemas.

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?

Explicitly names when to use it ('a single fact or short answer') and names two alternatives with their selecting conditions: webscraping_ai_fields for several named values at once, webscraping_ai_text to read/reason over the whole page yourself. It also states the failure condition that produces an error.

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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