tokenguard Web Extraction

x402

tokenguard Web Extraction is a paid API for AI agents from eltociear-tokenguard.hf.space, paid per call via x402, $0.05/call, status unknown (last checked 2026-09-14).

Fetches a URL or accepts raw text and converts it into structured JSON matching a caller-supplied schema shape

Price / call$0.05

Seller volume →—

Success—

StatusUnknown

How this service works

Turn messy text — or any URL's page content — into structured JSON matching the shape you ask for, so an agent can consume a web page as data instead of prose

Output

A structured JSON object whose keys and value types conform to the schema_hint provided by the caller. When a URL is given, the endpoint first fetches the page content, then applies LLM-powered extraction to produce the requested fields. When raw text is provided, extraction runs directly on that text. The response contains only the requested data in machine-consumable form.

Example use cases

Product page price extraction

“Grab the product page at https://www.bestbuy.com/site/apple-macbook-pro/123456.p and extract the product name, current price, availability status, and star rating into a JSON object.”

Job listing parser

“Parse this job posting text I'm pasting and pull out the job title, company name, location, salary range, and required skills as a structured JSON object: 'Senior Software Engineer at Acme Corp, remote, $150k-$180k, requires Python and AWS...'”

News article metadata extraction

“Fetch the article at https://techcrunch.com/2024/01/15/some-article and extract the headline, author, publication date, and a one-sentence summary into JSON with keys title, author, publishedAt, and summary.”

Technical details

Endpoint

POSThttps://eltociear-tokenguard.hf.space/llm/extract

When to prefer

Choose this endpoint when you need to consume an arbitrary web page or unstructured text blob as typed, schema-conforming JSON in a single API call. It is especially useful when the target page has no native API, when the schema varies per use case (since the caller supplies it at runtime), or when you want to avoid building and maintaining a bespoke scraper. Prefer alternatives if you need JavaScript rendering of a heavy SPA, real-time monitoring of page changes, or bulk batch extraction across hundreds of URLs.

Side effects

URL is inaccessible, paywalled, or returns a non-200 status — extraction returns empty or partial results
schema_hint is too complex or ambiguous — model may hallucinate fields or mismap values
Page content is mostly JavaScript-rendered (SPA) and the fetcher sees only an empty shell
Extracted values are plausible but factually wrong if the source text is ambiguous
No text or URL provided — request fails with a validation error
Rate limiting or HF Space cold-start increases latency significantly

Input

Schema · Request body

Field	Type	Description
url	string	Page to fetch and extract from (used when text is omitted)
text	string	Raw text to extract from
instruction	string	Optional extra extraction instruction
schema_hint	object	Target JSON shape

Output

Schema · Response

{
  "type": "json",
  "example": {
    "data": {
      "plan": "Pro",
      "price_usd": 20
    },
    "provider": "llm7",
    "source_url": "https://example.com/pricing"
  }
}

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How this service works

Turn messy text — or any URL's page content — into structured JSON matching the shape you ask for, so an agent can consume a web page as data instead of prose

Output

Example use cases

Product page price extraction

“Grab the product page at https://www.bestbuy.com/site/apple-macbook-pro/123456.p and extract the product name, current price, availability status, and star rating into a JSON object.”

Job listing parser

News article metadata extraction

Technical details

Endpoint

POSThttps://eltociear-tokenguard.hf.space/llm/extract

When to prefer

Side effects

URL is inaccessible, paywalled, or returns a non-200 status — extraction returns empty or partial results
schema_hint is too complex or ambiguous — model may hallucinate fields or mismap values
Page content is mostly JavaScript-rendered (SPA) and the fetcher sees only an empty shell
Extracted values are plausible but factually wrong if the source text is ambiguous
No text or URL provided — request fails with a validation error
Rate limiting or HF Space cold-start increases latency significantly

Input

Schema · Request body

Field	Type	Description
url	string	Page to fetch and extract from (used when text is omitted)
text	string	Raw text to extract from
instruction	string	Optional extra extraction instruction
schema_hint	object	Target JSON shape

Output

Schema · Response

{
  "type": "json",
  "example": {
    "data": {
      "plan": "Pro",
      "price_usd": 20
    },
    "provider": "llm7",
    "source_url": "https://example.com/pricing"
  }
}