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web_page_markdown

Fetch web pages or search results and convert them into clean Markdown for AI agents and RAG pipelines, enabling reliable content ingestion without browser overhead.

Instructions

RAG Web Browser Lite — Cheap Markdown for AI Agents

Web search and fetch tool for AI agents and RAG pipelines. Queries Google Search, scrapes the top N pages over raw HTTP, and returns clean Markdown. $0.001 per page plus small platform usage (raw HTTP, no browser).

Pricing (pay per event, billed to your Apify account): Page extracted: $0.001; platform usage billed to your account.

Runs on Apify as subimpact/rag-web-browser-lite.

Input Schema

NameRequiredDescriptionDefault
glNoTwo-letter country code for Google Search, e.g. us, my, uk, sg.us
hlNoTwo-letter language code for Google Search, e.g. en, ms, zh.en
queryNoEnter Google Search keywords or a URL of a specific web page. Examples: - <code>san francisco weather</code> - <code>https://www.cnn.com</code> - <code>function calling site:openai.com</code>https://example.com
_maxItemsNoHow many dataset items to return to the model (1-1000, default 50). Truncates the response only; it does not change what the Actor collects or charges for.
maxResultsNoThe maximum number of top organic Google Search results whose web pages will be extracted. If `query` is a URL, this field is ignored and the Actor only fetches the specific web page.
_maxChargeUsdNoPer-run spending cap in USD (default 0.5). Passed to Apify as the run option maxTotalChargeUsd, so the run stops when it reaches this charge.
outputFormatsNoSelect one or more formats to which the target web pages will be extracted and saved in the resulting dataset.
serpMaxRetriesNoThe maximum number of times the Actor will retry fetching the Google Search results on error.
serpProxyGroupNoApify Proxy group used for fetching Google Search results.GOOGLE_SERP
proxyConfigurationNoApify Proxy configuration used for scraping the target web pages.
removeElementsCssSelectorNoA CSS selector matching HTML elements that will be removed from the DOM before converting to text, Markdown, or saving as HTML. Set to a non-existent selector like `dummy_keep_everything` to disable removal.nav, footer, script, style, noscript, svg, img[src^='data:'], [role="alert"], [role="banner"], [role="dialog"], [role="alertdialog"], [role="region"][aria-label*="skip" i], [aria-modal="true"]

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.0

TDQS

A3.5/5.0
Behavior4/5

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

With no annotations, the description carries the full burden and does substantial work: it discloses the per-page cost ($0.001), that billing goes to the Apify account, and critically that extraction is raw HTTP with no browser, which tells the agent JS-rendered pages may fail. It stops short of describing return structure, failure/blocking behavior, or how run outcomes are surfaced.

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

Conciseness3/5

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

The core purpose is front-loaded in the first two sentences, but pricing is stated twice (prose and a separate 'Pricing' block) and the closing 'Runs on Apify as subimpact/rag-web-browser-lite' is packaging rather than decision-relevant information.

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?

For an 11-parameter, zero-annotation tool with no output schema, the description covers the cost model and the no-browser constraint but never explains what a returned item looks like (SERP metadata plus page content per outputFormats) or what happens on partial failures. That leaves the agent guessing about result shape despite the cost-based, per-page billing model.

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 coverage is 100%, so the schema already documents all 11 parameters in detail, including maxResults being ignored when query is a URL. The description adds no parameter-level meaning beyond what the schema states, so the baseline 3 applies.

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 states a specific verb pair and resource: it queries Google Search, scrapes the top N pages, and returns Markdown. This corrects an uninformative tool name (web_page_markdown) by revealing that search is part of the operation. It does not, however, explicitly contrast itself with the google_search or seo_page_audit siblings, so an agent must infer the distinction.

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

Usage Guidelines3/5

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

Usage is implied rather than stated: 'for AI agents and RAG pipelines' plus the query field taking either keywords or a URL hints at search-then-fetch use cases. There is no explicit when-to-use/when-not guidance and no mention of when to prefer the plain google_search sibling instead of paying for page extraction.

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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