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quantumproxies

quantumproxies-mcp

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scrape

Fetch a single web page through a residential proxy and return clean Markdown, escalating to a real browser when bot-protected. Supports structured or AI extraction for targeted data.

Instructions

Scrape a single web page through a residential proxy and return it as clean Markdown (or HTML/text). Uses a real Chrome TLS fingerprint by default and only spins up a headless browser if the page is bot-challenged. Optionally run structured extraction (CSS selectors) or AI extraction (natural-language prompt). Markdown keeps the complete page by default (content_mode 'smart': everything except nav/footer/cookie chrome, with GFM tables and absolutized links); to inspect a page's raw no-JS/SEO fallback use format 'html'.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
tocNoPrepend a table of contents built from the page headings
urlNoThe page URL to scrape (optional only when you pass `html` to convert)
xhrNoRecord the page's XHR/fetch traffic (URL, method, status, response body) into payload.xhr. Forces a browser render. An SPA's own JSON API is usually far cleaner than its DOM — use this to DISCOVER the API, then fetch_resource to return it directly.
htmlNoConvert HTML you already have instead of fetching: no proxy bandwidth is used, and the full parser pipeline still applies. Pass `url` too if you want relative links absolutized.
modeNosummary: return only metadata (title, description, canonical, contentLength, status, engine, bytes) with no page content — use this when auditing pages instead of reading them
chunkNoSegment the output into payload.chunks[] for RAG/vector-DB ingestion — each chunk carries its heading path and token count. Fences and tables are never split.
queryNoWhat you are looking for on the page. Keeps only the relevant sections (BM25 scoring over blocks, headings preserved) — the way to read one fact off a huge page without spending its whole token budget.
engineNoauto (default): TLS tier, escalate to browser on block. tls: never escalate — exactly what a pure HTTP bot (no JS) sees, right for SEO checks. render: force browser.
formatNoOutput format (default markdown)
parserNoYour own parsing rules, as CSS selector lists — use these when you know the page and don't want to rely on heuristics. include: keep ONLY these subtrees (targeted extraction, e.g. ['article.post']). exclude: delete site-specific chrome we kept. keep: protect a section (sidebar, dialog, form) that smart mode would strip.
renderNoForce the headless browser (JS execution)
actionsNoOrdered browser interactions before capture (forces a render). Each is one object: {"click":"#sel"}, {"clickText":"Accept"} (click by visible text — dismiss a consent wall without knowing its CSS), {"type":{"selector":"#q","text":"shoes"}}, {"scroll":"bottom"}, {"wait":1000}, {"waitForSelector":".results"}. Add "optional":true to skip a miss, or "timeoutMs":N to bound one action.
cookiesNoCookies to send as name→value — the simple way to scrape behind a login
countryNoISO country code for the proxy exit, e.g. 'us'
extractNoStructured-extraction schema: { field: "css selector" | { selector, attr, all, fns } }. `fns` is a transform pipeline run on the value — e.g. { "price": { "selector": ".price", "fns": ["amount_from_string"] } } returns a number, not text. Functions: amount_from_string, amount_range_from_string, convert_to_float/int/str, trim, lower, upper, {regex_search|regex_find_all: "pat"}, {replace:{from,to}}, {join:","}, {select_nth:0}, length, unique, max, min, average, product.
formatsNoAdditional formats to return together in payload.formats, e.g. ['markdown','text']
ai_promptNoNatural-language instruction — the LLM turns the page into structured JSON
ai_schemaNoJSON Schema for deterministic AI extraction; returned under payload.ai.data
app_stateNoMine the page's own hydration state (Next.js __NEXT_DATA__, Nuxt, embedded JSON islands) into payload.metadata.appState. This is where SPAs keep the real data — prices behind a picker, stock, download counts, listings — even when the DOM shows only a shell, so it often answers the question without a browser render. true/'auto': pruned to the informative parts (recommended). 'raw': the complete blobs, up to 512KB.
preset_idNoRun a stored parser preset (see save_parser_preset) instead of passing `extract` selectors. Results land in payload.data exactly the same way, and the run is scored so the preset can detect decay and self-heal.
highlightsNoWith `query`: also return the N most relevant passages in payload.highlights
links_modeNoLink rendering. inline (default): [text](url). footnote: URLs moved to a numbered reference list at the end. strip: keep only the link text — cuts 30-48% of the tokens on link-dense pages when you only need the prose.
max_tokensNoCap the markdown at ~this many tokens, cutting at a section boundary (never inside a table or code block) and noting how much was omitted
frontmatterNoPrepend YAML front-matter (title, url, canonical, description, author, date) so the markdown is self-contained for RAG/Obsidian pipelines
images_modeNoinline (default) keeps ![alt](url); 'alt' keeps only alt text; 'strip' removes images
content_modeNosmart (default): whole page minus nav/footer/cookie chrome. article: Readability main article only (news/blogs). full: entire body as-is.
content_modesNoReturn several content scopes from ONE fetch under payload.contents (e.g. compare smart vs full)
include_linksNoReturn all de-duplicated absolute page links in payload.links
reveal_hiddenNoRender tier only: before capturing, open <details>/accordions and click through every tab, appending each revealed panel to the page. Use it for tabbed code samples or spec accordions where a plain render captures only the visible variant.
fetch_resourceNoRegex matched against the page's network requests: the first matching response's BODY becomes the result instead of the page HTML (e.g. '/api/products' to get an SPA's JSON directly). Forces a render. Fails with 504 if nothing matches.
summary_sectionsNoAppend 'Links on this page' / 'Images on this page' sections — handy when deciding the next hop
Behavior5/5

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

With no annotations present, the description carries the full disclosure burden and exceeds it. It reveals the residential proxy usage, Chrome TLS fingerprint default, conditional headless-browser escalation, default Markdown behavior, and content_mode 'smart' details. This gives an agent an accurate mental model of how the tool will actually behave, including escalation and output preprocessing.

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 dense but deliberately structured: core purpose first, then network/browser behavior, then optional extraction and output defaults. It avoids filler, though one or two long sentences pack a lot of clauses. For a tool with this many capabilities, it is appropriately sized and front-loaded.

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, together with the fully documented schema, covers the main behaviors, output formats, extraction options, and important edge-case choices like engine escalation and raw HTML fallback. It does not define a high-level response envelope or error semantics, but the parameter-level payload descriptions compensate well. Given the tool's complexity, this is a high-quality, complete definition.

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%, and the schema itself contains rich explanations for all 31 parameters. The main description adds strategic framing for a few features (e.g., extraction via CSS selectors or natural language) but does not need to re-document parameters. This is the appropriate baseline-3 case where the schema carries the heavy lifting.

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 a precise verb and resource: 'Scrape a single web page through a residential proxy.' It clearly distinguishes this tool from the crawl and search siblings by emphasizing single-page scraping. It also enumerates the major processing modes (Markdown, HTML, text, structured extraction, AI extraction) without ambiguity.

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 establishes clear context: it is for one page, with optional extraction modes. It names helpful routing details such as 'use this to DISCOVER the API, then fetch_resource to return it directly' and distinguishes raw no-JS/SEO fallback via format 'html'. It does not explicitly state when to prefer crawl or search_and_read instead, but the single-page framing and sibling names make the intended usage reasonably inferable.

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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