conqueror-keyword-clustering

Group keywords into page-level clusters and map each cluster to an existing or new page, flagging cannibalization. Use when you have a keyword list or Search Console data and need a content plan. Requires a connected Conqueror MCP server.

Goal

Group keywords into page-level clusters and decide which existing or new page should target each cluster. This is a keyword mapping workflow, not just a semantic grouping exercise.

Required inputs

• projectId
• A keyword list, saved keyword tag, seed topic, or target domain
• Optional existing URLs/pages to map against

If keywords are not provided, use list_saved_keywords for saved sets, research_keywords for seed discovery, or get_ranked_keywords when the user starts from a target domain.

Conqueror MCP tools

• list_saved_keywords: fetch an existing keyword set, optionally filtered by tags.
• research_keywords: expand a seed when the user starts from a topic.
• get_ranked_keywords: gather exact ranking keywords and URLs when the user starts from a domain or page.
• get_search_console_performance: when Search Console is connected, pull real queries with dimensions: ["query","page"] to map terms to the pages already earning impressions and to surface cannibalization (one query splitting clicks across multiple URLs).
• get_serp_results: validate whether keywords belong on the same page by checking SERP overlap and intent.
• get_local_serp_results: use for local SEO clusters when Maps/local-pack intent should affect page mapping.
• save_keywords: optionally tag final clusters after user confirmation.

Workflow

1. Gather the candidate keyword set.
   • Use get_search_console_performance (dimensions ["query","page"]) when Search Console is connected to start from real queries and the pages already ranking for them.
   • Use get_ranked_keywords for domain/page-driven clustering.
   • Use search_local_businesses and get_local_serp_results when proximity, local packs, or Google Business results determine whether terms belong on location pages.
2. Remove duplicates, irrelevant terms, and terms that clearly require a different product or audience.
3. Build clusters around intent and page type:
   • Same SERP intent and similar ranking pages belong together.
   • Different intent, buyer stage, or SERP format should be split.
   • Similar words do not guarantee the same cluster.
4. For important borderline terms, use a small get_serp_results batch to check overlap.
5. Assign each cluster to:
   • Existing URL, if supplied and appropriate
   • New page recommendation, if no existing page fits
   • Do-not-target / later bucket, if weak or off-strategy
6. Identify cannibalization risk when multiple pages would target the same intent. When Search Console is connected, confirm it from real data with get_search_console_performance (dimensions: ["query","page"]) — the same query sending impressions to multiple URLs.
7. Ask before applying cluster tags with save_keywords.

Output format

Start with a short mapping summary:

• Number of clusters
• Pages to create
• Existing pages to update
• Cannibalization or consolidation issues

Then include:

Cluster	Primary keyword	Secondary keywords	Intent	Target page	Priority	Notes

For each cluster, include a recommended page brief:

• Page type
• Searcher problem
• Required sections
• Internal-link opportunities
• Save/tag suggestion

Guardrails

• Do not over-cluster tiny keyword sets. If there are fewer than 10 usable terms, produce a simple map.
• Do not rely on lexical similarity alone. SERP intent wins.
• Do not replace tags broadly without explicit confirmation.
• If existing URL data is missing, label target pages as proposed.