Inventory parameter behavior in an exported category sample
Classify supplied filter, action, sort, pagination, tracking, and unknown keys without claiming to discover the site's full faceted URL space.
Free, no signup. Paste category parameter URLs to classify filters, actions, sort, pagination, tracking, and unknown keys. The audit stays in your browser and never crawls beyond the supplied list.
No evidence bundle loaded.
Runs entirely in your browser — nothing you paste is uploaded or stored. Anonymous run-level outcome counters may be used for aggregate research; URLs, domains, IPs, and identifiers are never included, and no statistic is released below 100 runs.
…/shoes?color=blue&size=10: filter, filter
…/shoes?compare=123: action
…/shoes?sort=price&page=2: sort, pagination
…/shoes?utm_source=newsletter: tracking
Advice: Remove crawlable action URLs where possible, then review filter combinations separately.
Saved targets, named lists, and recent check summaries remain only in this browser.
The browser parses each supplied URL with the native URL API, classifies every query-key name through explicit regular-expression rules, and applies row-level and grouped-pattern warnings. Calendar groups over 60 unique URLs and other groups over 100 are labeled with the same deterministic caps used by the crawl frontier. If any row includes a recognized filter, it returns the canonical/noindex review reminder. No requests leave the browser.
Parameter names do not reveal content value or indexability. The tool does not crawl links, test robots/canonicals/noindex, read logs, or observe Google. Pattern counts apply only to the pasted list; the row warning and grouped thresholds can flag legitimate URLs or miss traps outside the sample.
Faceted navigation lets users filter or sort a listing by attributes such as color, size, brand, or price. Each combination can create a distinct parameter URL.
It recognizes common filter names, action parameters such as add-to-cart, compare, and wishlist, sort/order/view, page/p/offset, and tracking parameters such as utm_, gclid, and fbclid. Everything else is labeled unknown.
No. Some combinations deserve crawlable landing pages, while others should be consolidated or blocked from crawling. Decide from demand, uniqueness, internal links, and crawl behavior rather than applying one rule universally.
No. It audits only the absolute URLs you paste, up to 150 rows. It cannot discover combinations, measure crawl volume, or confirm directives.
A supplied URL with more than five query parameters is labeled Too many parameters. That is a simple row-level warning, not proof of an active crawl trap.
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Where this tool helps
Classify supplied filter, action, sort, pagination, tracking, and unknown keys without claiming to discover the site's full faceted URL space.
Surface rows with more than five parameters and supplied calendar or high-cardinality groups as deterministic review cues rather than proof of a crawl trap.
Add explicit crawlability, indexability, approved-landing, accessibility-tree, and keyboard evidence to describe the first supplied interaction more precisely.
Annotate exact matching URLs from a sanitized Site Audit or Indexation audit bundle while preserving the bundle's source and snapshot time.
Use the dashboard and row evidence to prioritize log, crawl, index coverage, demand, canonical, noindex, robots, and internal-link verification outside the tool.
Watch the full workflow
This beginner walkthrough audits eight fictional shopping-category U-R-Ls. We will explain what a facet and query parameter are, load a prior audit note, record interaction observations, review six simple parameter classes, inspect one complex-U-R-L warning, and finish with the evidence needed before changing a live navigation system.
Faceted navigation lets visitors narrow a listing by choices such as color, size, price, or brand. Those choices often create U-R-L query parameters after the question mark. The auditor groups parameter names into filters, actions, sorting, pagination, tracking, or unknown. It analyzes only the U-R-Ls you paste, runs locally, and does not crawl the website.
Use it to organize a category-U-R-L export, find repeated or unusually complex patterns, distinguish approved search landing pages from temporary states, attach an earlier audit observation, or create a review queue for technical and product teams. The report helps decide what to investigate. It does not apply one universal crawl or indexation policy.
Paste up to one hundred fifty complete H-T-T-P or H-T-T-P-S U-R-Ls, one per line. An optional J-S-O-N bundle can add dated evidence from an earlier Site Audit or Indexation Audit when a U-R-L matches exactly. The interaction section stores observations you collected from a real control. None of these inputs triggers a new crawl or network request.
The basic workflow is paste, audit, review parameter types and warnings, and then validate decisions with server logs, crawl data, index coverage, and search demand. That final evidence step matters because the same filter can be valuable on one site and wasteful on another.
Paste eight fictional shoe-category U-R-Ls. The first is a clean category with no query string. The others include color and size filters, compare and wishlist actions, campaign tracking, sorting and page number, an unknown promo code, six filters together, and an in-stock grid view. This small sample makes every class visible, but it is not a complete site inventory.
Select the fictional Site Audit bundle. Its note shows when that snapshot was created. The auditor attaches the record only to an exactly matching U-R-L. It does not revisit the page or refresh its status, canonical, indexability, or explanation. Keep the source and date attached so nobody mistakes imported evidence for a live check.
Open interaction evidence and record that this control is a crawlable link, its result is intended for indexation, and the state is an approved static landing page. Add initial and expanded accessibility-tree observations plus operable keyboard behavior. These are supplied observations, not a live accessibility test.
Select Audit facets. The browser creates eight result rows and a parameter dashboard without requesting any U-R-L. One row receives a simple too-many-parameters review cue. Filters are present, so the workbench says review. The larger grouped warnings do not appear because eight U-R-Ls are far below their thresholds.
The dashboard finds sixteen total parameter appearances: nine filters, two actions, two sort controls, and one each for pagination, tracking, and unknown. A single U-R-L can contribute more than one parameter, which is why the total exceeds eight. These counts describe only our pasted sample. They help focus review but do not prove duplication or wasted crawling.
The parameter inventory sorts by frequency. Color and size each appear on two URLs; brand, compare, in-stock, material, page, price, promo code, rating, sort, UTM source, view, and wishlist each appear once. Frequency helps prioritize, while each key still needs a documented business purpose.
Parameter depth shows one clean URL, two URLs with one parameter, four with two parameters, and one with six. Depth is a compact complexity signal. It cannot tell whether a combination has unique content, internal links, demand, index coverage, or meaningful crawl volume.
The base category has no recognized parameters and receives the one exact bundle match: status two hundred, canonical to itself, indexable true, and a fictional approved-category reason. Treat this as dated imported evidence. The tool neither fetches the URL nor confirms that the prior observation remains true.
Color and size classify as filters, as do brand, price, material, rating, and in-stock. Classification comes from transparent parameter-name rules. It does not inspect rendered content or prove whether a combination should consolidate, remain crawlable, or become an approved search landing page.
Compare and wishlist classify as actions, while UTM source is tracking. Crawlable state-changing actions often deserve early investigation because crawlers should not trigger customer controls. Confirm the actual element, request behavior, internal links, and server handling before changing templates.
Sort and view classify as sort controls, page is pagination, and promo code remains unknown. Unknown means no rule matched, not that the parameter is harmless. Document its purpose, inspect how it changes content and links, and include it in crawl and log analysis.
The U-R-L with six filters crosses the simple threshold of more than five parameters, so its row says Too many parameters. That does not prove an active crawl trap. A useful landing page can cross the threshold, while a dangerous repeating pattern outside this sample may never appear. Treat the label as a reason to collect stronger evidence.
The interaction result is approved static landing, with initial and post-expansion accessibility trees captured and keyboard marked operable. This classification follows the supplied checkboxes and text exactly. It does not operate the real control, validate focus management, or prove that the landing-page approval is correct.
Because actions are present, the advice prioritizes removing crawlable action URLs where appropriate, then reviewing filter combinations separately. Use the cited rows to assign owners and collect server logs, full crawl evidence, index coverage, internal links, content value, accessibility behavior, and demand before implementing policy.
The browser uses the native URL parser, explicit name rules, a more-than-five row cue, calendar groups over sixty unique URLs, and other groups over one hundred. It supports six classes, safe output, a built-in example, actionable validation, an optional sanitized bundle, and at most one hundred fifty supplied URLs.
A parameter name cannot reveal whether a page has useful content or belongs in search. This tool does not crawl, test robots rules, canonicals, or noindex, read server logs, observe Google, measure demand, or find combinations missing from your sample. Export a representative inventory, inspect the real controls, validate live directives, and combine crawl, log, indexation, content, accessibility, and demand evidence before changing production.
Use the highlighted action U-R-Ls, unknown parameter, and six-filter row as places to begin investigating. Then combine a complete crawl, server logs, index coverage, canonical and robots checks, internal links, unique-content review, accessibility testing, and search demand. The sample organizes questions; it does not decide which live pages belong in search.