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ScrapeUnblocker MCP Server

ScrapeUnblocker MCP Server

Official
by ScrapeUnblocker
OverviewSchemaSourceRelated ServersDiscussionsAdmin
TypeScript
Remote

Fetch AI-parsed page data

fetch_parsed
Read-only

Extracts structured JSON from pages via ScrapeUnblocker, converting product, listing, or article content into parsed fields without HTML parsing. Returns rendered HTML if no structured data is found.

Instructions

Fetch a web page through ScrapeUnblocker and return AI-parsed structured JSON instead of raw HTML (e.g. product details, article content). Best for extracting fields from product, listing or article pages without writing your own HTML parsing. If the page holds no structured data, the result says so and returns the rendered HTML instead (billed like fetch_html).

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "url": "<url>"
    },
    "name": "fetch_parsed"
  }
}

Input Schema

NameRequiredDescriptionDefault
urlYesThe absolute URL to fetch and parse.
rules_hintNoOptional natural-language hint about what to extract, to guide parsing.
proxy_countryNoOptional ISO country code to route through, e.g. 'US'.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changedv0.12.5
    • removedInput schema / additionalProperties
      Removed value: -false
  2. Addedv0.1.4
  3. Removedv0.1.3
  4. First observedv0.1.2

TDQS

A4/5.0
Behavior4/5

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

Annotations already cover read-only, open-world, non-destructive semantics, and the description adds genuinely useful behavior: a fallback path that returns rendered HTML when no structured data is found, plus a billing consequence ('billed like fetch_html'). Missing are any notes on failure modes, blocking, or latency for a scraping tool.

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?

Three sentences, front-loaded with the core action and output type, then the use case, then the edge-case behavior. No filler and every sentence carries distinct information.

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?

With three fully described parameters, rich annotations, and no output schema, the description does the one thing needed: it explains the shape of the return value (structured JSON, or rendered HTML fallback). It leaves proxy/geo routing and error conditions unexplained, which keeps it short of fully complete.

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 url, rules_hint, and proxy_country are all documented in the schema itself. The description adds only indirect context ('AI-parsed', 'guide parsing' implied) and never mentions rules_hint or proxy_country, so it does not exceed the schema's contribution.

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?

States a specific verb+resource (fetch a web page) plus the distinctive output contract: AI-parsed structured JSON rather than raw HTML, with example categories (product details, article content). The phrase 'instead of raw HTML' implicitly distinguishes it from fetch_html/fetch_markdown, though it never names those siblings.

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?

Gives a clear use context — 'best for extracting fields from product, listing or article pages without writing your own HTML parsing' — and explains what happens when the page has no structured data. It stops short of explicitly routing the agent to fetch_html or fetch_markdown for raw/markdown retrieval.

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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