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Yes—Gemini can extract information from web pages, but it is not a general-purpose crawler. For pages you already know, pass their full public URLs to Gemini’s URL Context tool. For discovering public pages, enable Google Search grounding, then optionally send selected results through URL Context for deeper extraction. In both cases, request a defined schema, preserve the returned source annotations, and validate every value in your own code before storing or using it.
Choose the Gemini retrieval method first
Gemini has two documented web-retrieval routes, and they solve different problems.
| Need | Use | What it does | What it does not promise |
|---|---|---|---|
| You have the exact pages | URL Context | Retrieves content from URLs you include in the request so Gemini can extract, compare, or summarize fields. | It does not follow links found inside those pages. |
| You need public-web discovery | Google Search grounding | Searches the public web when the model decides search is useful and returns answer annotations associated with source URLs. | It does not guarantee exhaustive coverage or exactly one search per request. |
| Your corpus is private or specialized | External search API grounding on Vertex AI | Lets your endpoint return relevant snippets from your own index for Gemini to use. | The official overview does not choose a deployment, price, or architecture for your workload. |
Google describes URL Context as a way to “provide additional context to the models in the form of URLs.” Its Google Search documentation says that “Grounding with Google Search connects the Gemini model to real-time web content and works with all available languages.” Treat those as retrieval capabilities, not a promise that every page is current, accessible, or complete.
What you need before making a request
- A Gemini API or Vertex AI project and an API key or service account configured according to Google’s current setup instructions.
- Full URLs, including
https://, when using URL Context. - Publicly accessible pages. Login walls, paywalls, localhost, private networks, and tunneling services are not supported by URL Context.
- A precise extraction contract: fields, types, allowed values, and what to return when a field is absent.
- Application-side validation and storage for the URL-to-field evidence mapping.
URL Context can process up to 20 URLs in one request, and content retrieved from one URL can be at most 34 MB. Google lists HTML, JSON, plain text, XML, CSS, JavaScript, CSV, RTF, PNG, JPEG, BMP, WebP, and PDF among supported formats. YouTube URLs, Google Workspace files such as Docs and Sheets, audio/video files, and paywalled pages are listed as unsupported. Limits and supported-model lists can change, so check the live Gemini documentation before shipping a long-lived integration.
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Extract known pages with URL Context
1. Supply direct URLs, not a domain name
URL Context retrieves only the URLs in your request. It does not discover a site map or recursively visit links. Build the URL list yourself from a feed, sitemap, database, or earlier search step, and keep each URL associated with the record you intend to update.
2. Ask for fields and evidence
Tell Gemini exactly what to extract and how to represent missing data. Asking for a short evidence excerpt makes later review possible, but evidence is still text generated from retrieved content; it is not an independent verification.
Extract product information from each supplied page. Return one object per URL with exactly these fields:
{
"url": string,
"title": string,
"price": number|null,
"currency": string|null,
"availability": "in_stock"|"out_of_stock"|"unknown",
"evidence": string
}
Use null or "unknown" when the page does not state a value. Do not infer a price, currency, or availability.3. Constrain the response shape
Gemini’s structured-output feature can require fields and types, including when built-in tools such as URL Context are enabled. A schema makes parsing more predictable; it does not prove that values are correct, complete, or taken from the right page. Reject malformed output and validate business rules in ordinary application code.
Illustrative REST request
The exact model name and tool syntax depend on the Gemini API model currently enabled for your project. Use the supported-model table in Google’s documentation and keep the URL Context tool enabled in the request. The following shows the request pattern; replace the model identifier and credentials with values from your project.
curl -X POST
"https://generativelanguage.googleapis.com/v1beta/models/MODEL:generateContent?key=$GEMINI_API_KEY"
-H "Content-Type: application/json"
-d '{
"contents": [{
"role": "user",
"parts": [{"text": "Extract title, date and price from each supplied URL. Return JSON matching the requested fields. Use null when absent."}]
}],
"tools": [{"url_context": {}}],
"generationConfig": {
"responseMimeType": "application/json",
"responseSchema": {
"type": "ARRAY",
"items": {
"type": "OBJECT",
"properties": {
"url": {"type": "STRING"},
"title": {"type": "STRING"},
"date": {"type": "STRING", "nullable": true},
"price": {"type": "NUMBER", "nullable": true},
"evidence": {"type": "STRING"}
},
"required": ["url", "title", "date", "price", "evidence"]
}
}
}
}'Use the request format documented for the model you select; APIs evolve, and a model that supports structured output is not necessarily the same model listed for URL Context.
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Python validation pattern
import json
from decimal import Decimal
REQUIRED = {"url", "title", "date", "price", "evidence"}
def validate_records(text):
records = json.loads(text)
if not isinstance(records, list):
raise ValueError("Expected a JSON array")
for item in records:
if set(item) != REQUIRED:
raise ValueError(f"Unexpected fields for {item.get('url')}")
if not isinstance(item["url"], str) or not item["url"].startswith("https://"):
raise ValueError("Invalid URL")
if item["price"] is not None and (not isinstance(item["price"], (int, float)) or item["price"] < 0):
raise ValueError("Invalid price")
return recordsKeep the original URL beside every extracted object. Check required fields, date formats, duplicate URLs, currency consistency, and outliers. A missing value should remain missing; do not silently convert a retrieval failure into a negative fact.
Discover pages with Google Search grounding
Use Search grounding when you do not know the relevant URLs. Gemini may decide whether to search, issue one or more queries, synthesize the results, and return annotations associating answer segments with URLs. Because the number of searches is model-decided, do not build logic that assumes exactly one query.
A reliable discovery workflow
- Describe the page type and constraints, such as “official pricing pages for vendors that support SSO,” rather than asking for “everything on the web.”
- Ask for a candidate list containing URL, page title, and the reason the page matches.
- Read the returned URL annotations and preserve the URL attached to each claim.
- Deduplicate and filter candidates in your code.
- Send the selected public URLs to a second URL Context request for field-level extraction.
Google documents combining Search grounding with URL Context. This search-then-inspect pattern gives you discovery and deeper page access without claiming that Gemini crawled an entire domain.
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Yes, within URL Context’s documented limit of 20 URLs per request and 34 MB of retrieved content per URL. For larger batches, partition the input, record a job ID and source list, and retry only failed partitions. Do not assume that a page appearing in one batch will be interpreted identically in another; include the same extraction instructions and schema every time.
Batching checklist
- Use stable, canonical URLs and remove duplicates before submission.
- Keep batches below the documented URL limit.
- Record request time, model, prompt version, and URL list.
- Store per-URL status as retrieved, unavailable, unsupported, or validation_failed.
- Reprocess failed URLs separately instead of treating them as empty pages.
Can Gemini return website data as JSON?
It can return schema-constrained JSON when your selected model and API route support structured outputs. Define primitive types, enumerations, nullable fields, and required properties. Still parse the response as untrusted input: JSON validity says nothing about whether a price belongs to the page, whether a date is current, or whether a field was omitted because retrieval failed.
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Evidence-aware record design
For each field that matters, store the source URL and a short evidence string or the returned citation annotation. Keep the raw response for auditability, but avoid treating an evidence excerpt as proof without checking the page and your own business rules.
Access, freshness, and coverage limits
Retrieval access
URL Context requires public access and full URLs. It cannot use your browser session, private network, localhost, or a login cookie. A page that renders only after an interactive login, or content hidden behind a paywall, may be unavailable.
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Freshness
Google documents a two-stage URL Context process: it first attempts an internal index cache and falls back to a live fetch when a URL is unavailable there. That is implementation behavior, not a freshness guarantee for every page. If recency matters, capture the retrieval timestamp and verify critical values against the page or an official API.
Site-wide collection
The documented tools do not promise exhaustive crawling, crawl scheduling, robots handling, or robust extraction from arbitrary dynamic sites. For recurring, large-scale collection, evaluate a dedicated crawler, a site-provided API, or your own search index. Respect the target site’s terms, access controls, and applicable rules; permission depends on the site and your use case.
Private or specialized corpora
When the required information is not public or is poorly represented in general search, Google Cloud documents grounding Gemini with an external search API on Vertex AI. Your endpoint returns relevant snippets from your own sources, and Gemini uses them as grounding context. This is a separate integration route from public Google Search and URL Context. Design authentication, indexing, freshness, costs, and access policy yourself; the overview documentation does not select those choices for you.
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Common failures and fixes
| Symptom | Likely cause | Fix |
|---|---|---|
| No content returned | URL is private, paywalled, unsupported, malformed, or blocked. | Open the full HTTPS URL anonymously, remove login-dependent pages, and mark the record unavailable rather than empty. |
| Some URLs are missing | More than 20 URLs, oversized content, or a per-page retrieval failure. | Batch below the documented limit, split large pages, and retry failed URLs individually. |
| JSON parses but fields are wrong | Schema controlled shape, not truth. | Require evidence, validate types and ranges, compare against page text, and route outliers for review. |
| Search answers lack expected sources | The model decided search was unnecessary or results did not support the claim. | Ask for source-backed candidates, inspect annotations, and use URL Context on selected URLs. |
| Nested pages were not collected | URL Context does not follow links. | Discover links separately through Search, a sitemap, or your own crawler, then submit explicit URLs. |
| Results change between runs | Web content, search results, model behavior, or cached retrieval changed. | Log timestamps and model versions, cache accepted records, and revalidate high-impact fields. |
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curl -G "https://api.screenshotneo.com/v1/shot" -d access_key=YOUR_API_KEY --data-urlencode url=https://stripe.com -o shot.webpimport requests
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r.raise_for_status()
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FAQ
Does URL Context crawl a whole website?
No. You must provide each URL explicitly; nested links are not retrieved.
Is a grounded citation proof that the extracted value is correct?
No. Citations show which source supported an answer segment. Validate the value and retain the source-to-field mapping yourself.
Should I use Search grounding for a private database?
No. Use an external search API grounding design on Vertex AI when your corpus is private or specialized.
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Can I rely on Gemini for scheduled, exhaustive collection?
Not from the documented guarantees. Assess a dedicated crawler, site API, or custom index for recurring site-wide work.
Frequently Asked Questions
Does URL Context crawl a whole website?
No. You must provide each URL explicitly; nested links are not retrieved.
Is a grounded citation proof that the extracted value is correct?
No. Citations identify supporting sources, but your application must validate values.
Can Gemini access pages behind my login?
URL Context requires publicly accessible URLs and does not use your private browser session.
Quick Recap
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