---
name: keyword-clustering
description: Cluster keywords by intent and map them to existing or proposed pages.
---

# Searchmetrica Keyword Clustering

## 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.

## Searchmetrica 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.