Faceted Navigation Auditor

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.

Add interaction and accessibility-tree evidence

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.

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Found something broken in Faceted Navigation Auditor? Let us know what happened — this goes straight to a private triage queue, not a public list.

What will be sent
 No tool inputs, uploads, pasted source, complete results, query parameters, or URL fragments are attached automatically. You can edit or remove the selected passage above. Browser and anti-abuse metadata is processed for spam prevention. 

Sample report Built-in deterministic example

…/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.

How to use it

  1. Paste up to 150 absolute HTTP(S) category URLs, one per line.
  2. Select Audit facets, or load the built-in deterministic example.
  3. Review each parameter type and any over-parameterized row.
  4. Use server logs, crawl data, index coverage, and search demand before deciding which combinations should be indexable.

What the results mean

  • filter — common product/category attributes such as color, size, brand, price, or category.
  • action — add-to-cart, remove, compare, wishlist, quantity, or buy controls that should be reviewed for crawlable links.
  • sort — sort, order, or view controls.
  • pagination — page, p, or offset keys.
  • tracking — common campaign/click identifiers.
  • unknown — no rule matched; inspect its purpose manually.
  • trap — a row has more than five parameters, or the supplied list crosses the shared calendar/high-cardinality pattern thresholds.

How it works

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.

Features

  • Up to 150 supplied URLs.
  • Six transparent parameter classes, including common ecommerce filters and action controls.
  • Optional no-new-crawl evidence join from a shared audit bundle.
  • Safe DOM output for user-supplied URLs.
  • Built-in example and actionable validation errors.
  • Entirely client-side processing.

Limitations

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.

Frequently asked questions

What is faceted navigation?

Faceted navigationFaceted navigation (faceted search, product filtering) lets visitors refine a list of products or content by attribute — price, color, size, brand, rating. The SEO problem: each filter combination can spawn a distinct crawlable URL, turning a small catalog into millions of near-duplicate pages that waste crawl budget and dilute ranking signals. lets users filter or sort a listing by attributes such as color, size, brand, or price. Each combination can create a distinct parameter URL.

What parameters does the auditor recognize?

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.

Does every filter URL need noindex or a canonical?

No. Some combinations deserve crawlable landing pages, while others should be consolidated or blocked from crawlingCrawling is how search engines use automated bots (like Googlebot and Bingbot) to discover URLs and download pages. A page has to be crawlable to be indexed, but crawling on its own isn't a ranking factor.. Decide from demand, uniqueness, internal linksAn internal link is a hyperlink from one page on a website to another page on the same website. Internal links help search engines discover your pages and pass ranking signals (PageRank and anchor-text context) between them., and crawl behavior rather than applying one rule universally.

Does this tool crawl my navigation?

No. It audits only the absolute URLs you paste, up to 150 rows. It cannot discover combinations, measure crawl volume, or confirm directives.

What triggers the trap warning?

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.

Next stepSEO Incident Simulator — practice the same investigation on labeled fixtures.

Feature requests for Faceted Navigation Auditor

Upvote what you want most. New ideas can be submitted from the floating Feedback menu; requests appear here once approved, and the most-wanted rise to the top.

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Where this tool helps

Common use cases

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.

Triage over-parameterized and repeated URL patterns

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.

Separate approved landing pages from arbitrary states

Add explicit crawlability, indexability, approved-landing, accessibility-tree, and keyboard evidence to describe the first supplied interaction more precisely.

Join prior audit evidence without crawling again

Annotate exact matching URLs from a sanitized Site Audit or Indexation audit bundle while preserving the bundle's source and snapshot time.

Build a facet-control investigation handoff

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

Faceted Navigation Auditor walkthrough

Read the transcript

Faceted Navigation Auditor

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.

Step 1

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.

Step 2

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.

Step 3

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.

Step 4

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.

Step 5

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.

Step 6

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.

Step 7

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.

Step 8

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.

Step 9

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.

Step 10

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.

Step 11

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.

Step 12

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.

Step 13

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.

Step 14

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.

Step 15

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.

Step 16

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.

Step 17

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.

Step 18

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.

Step 19

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.

Step 20

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.

A parameter inventory starts the investigation. It does not finish it.

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.