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scrape

Read-only

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

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observed

TDQS

A4.3/5.0
Behavior5/5

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

Beyond the readOnlyHint/openWorldHint annotations, the description reveals useful behavioral details: residential proxy use, Chrome TLS fingerprint default, browser escalation only when bot-challenged, the smart content_mode default, and the raw no-JS/SEO fallback via format 'html'. This gives an agent a genuine model of how the tool behaves at runtime.

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 not bloated, front-loading the core action and output first. Each sentence adds meaningful context about defaults, render behavior, extraction, or format nuances, though the final sentence is a bit long.

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?

For a tool with 31 mostly-optional parameters and no output schema, the description provides a strong high-level mental model while the schema supplies detailed parameter semantics. It does not enumerate every output payload, but the parameter descriptions compensate well, so the overall context is sufficient for correct invocation.

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

Parameters4/5

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

Input schema coverage is 100%, so the schema carries most parameter meaning. The description still adds value beyond the schema, particularly by explaining that format 'html' is the raw no-JS/SEO fallback and by framing content_mode 'smart' as the default page-preserving behavior.

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 specific verb and resource: 'Scrape a single web page' and states the primary output ('clean Markdown (or HTML/text)'). It clearly distinguishes itself from site-level siblings like crawl and batch by emphasizing 'single' page.

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?

The description gives a clear context: scraping a single page with proxy, TLS fingerprinting, and optional extraction. However, it does not explicitly say when to choose scrape over crawl, search, or map, or when not to use it, so the routing guidance is mostly implied rather than stated.

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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TDQS

A4.1/5.0
Disambiguation5/5

Each tool targets a distinct resource or action: single scrape, batch scrape, crawl, search, dataset creation, parser lifecycle, proxy management, and SEO audit. Even the five status pollers are clearly differentiated by job type and their descriptions explicitly state which job they poll, so an agent can reliably select the right tool.

Naming Consistency4/5

Most names follow a verb-first pattern (create_dataset, generate_parser, run_collector, save_parser_preset, whitelist_ip) and listing tools consistently use the 'list_' prefix. However, a few are noun-first (parser_preset_stats, proxy_locations, collector_run_status) and the status polling tool for collectors breaks the otherwise consistent '<job>_status' convention ('collector_run_status' instead of 'run_collector_status').

Tool Count3/5

At 25 tools, the set is at the upper edge of the 'heavy' range. The tools all serve distinct functions, reflecting a broad platform covering scraping, crawling, search, datasets, parsers, proxies, and SEO, but the count borders on overwhelming for an agent, and some consolidation (e.g., a generic async job status endpoint) could reduce the surface.

Completeness4/5

The tool surface covers the core data-extraction lifecycle well: discovery (map, search), acquisition (scrape, batch, crawl), structured extraction (generate_parser, save_parser_preset, parser stats/heal), proxy management, and result aggregation (datasets, collectors). Notable gaps are the absence of any cancellation/abort mechanism for long-running async jobs and no way to delete a parser preset, but these are minor for most workflows.