Search Console Cannibalization Analyzer

Search Console Cannibalization Analyzer

AGENT ROLE: Autonomous Search Console analyst. Pull query→page performance, identify cannibalized queries deterministically, recommend a single canonical target per cluster, and emit the JSON in references/output.schema.json.

OBJECTIVE

For a verified GSC property and date range, find queries where ≥2 URLs receive impressions, quantify the resulting click loss and position instability, and recommend which URL to consolidate to.

INPUTS

• site_url (REQUIRED): exactly as verified in GSC. Domain property = sc-domain:example.com; URL-prefix property = https://example.com/.
• start_date,end_date (OPTIONAL): ISO dates. Default = last 28 complete days (GSC data lags ~2–3 days; never request today).
• country / search_type (OPTIONAL): default all / web.
• min_impressions (OPTIONAL, default 50): ignore queries below this to suppress noise.
• competitor_page_threshold (OPTIONAL, default 2): min competing URLs to flag.

AUTHENTICATION (Search Console API)

1. REQUIRE GOOGLE_APPLICATION_CREDENTIALS (service account) OR a user OAuth token with scope https://www.googleapis.com/auth/webmasters.readonly.
   • IF neither present THEN STOP error.code="AUTH_MISSING_CREDENTIALS".
2. The identity MUST be a verified owner/user of site_url in Search Console (API access is NOT implied by GA/GSC-UI access).
   • IF API returns 403 THEN STOP error.code="AUTH_NO_SITE_ACCESS": "Add the service-account email as a user on the Search Console property {site_url}."
3. Endpoint: POST https://searchconsole.googleapis.com/webmasters/v3/sites/{urlEncoded site_url}/searchAnalytics/query.

EXPECTED TOOL CALLS

• Run scripts/cannibalization.py --site {site_url} --start {d} --end {d} --min-impr {n}.
• The script pages the API (rowLimit=25000, startRow increments) with dimensions=["query","page"] and dataState="final".

PROCEDURE

STEP 1 — FETCH: query with dimensions=["query","page"]. Page until a response returns < rowLimit rows. STEP 2 — GROUP by query. Keep only queries with SUM(impressions) >= min_impressions. STEP 3 — DETECT: within a query, count URLs with impressions > 0. IF distinct_urls >= competitor_page_threshold THEN candidate cluster. STEP 4 — SCORE each cluster:

• primary = URL with the highest clicks; tie-break by best (lowest) average position.
• contenders = the other URLs.
• position_spread = max(avg_position) − min(avg_position) across URLs. Higher = more instability.
• estimated_lost_clicks = sum(contender_impressions) * CTR(primary_avg_position) − sum(contender_clicks), floored at 0. Use the standard position→CTR curve in references/ctr_curve.json.
• severity = high IF (position_spread ≥ 5 AND estimated_lost_clicks ≥ 10) ELSE medium IF estimated_lost_clicks ≥ 3 ELSE low. STEP 5 — RECOMMEND: consolidate_to = primary; action = "canonicalize/redirect contenders or differentiate intent". Never recommend deleting the primary. STEP 6 — EMIT JSON, clusters sorted by estimated_lost_clicks desc.

RATE LIMITS & ERROR HANDLING

• GSC API quota: ~1200 queries/min/property and a daily cap. IF 429/RESOURCE_EXHAUSTED THEN backoff 2^attempt (max 5) then STOP error.code="RATE_LIMITED".
• IF 400 invalid dimension/date THEN STOP error.code="BAD_REQUEST" echoing the message.
• Respect dataState: only trust final rows; discard fresh/partial days.

MISSING / INSUFFICIENT DATA

• IF total rows == 0 THEN status="no_data" (property may be new or date range empty); return empty clusters, no error.
• IF a query has URLs each with impressions but zero clicks THEN still evaluate (impression-level cannibalization); mark clicks_basis="impressions_only".
• Never invent CTR: if primary_avg_position > 20, cap CTR at the 20+ bucket value.

OUTPUT

One JSON object per references/output.schema.json. No prose.

FILES

• scripts/cannibalization.py — auth, paging, backoff, clustering, scoring.
• references/ctr_curve.json — position→CTR lookup used for lost-click estimation.
• references/output.schema.json — output contract.