seo-keyword-cluster

Example output: [examples/seo-keyword-cluster-headless-cms-20260514/PLAN.md](../../examples/seo-keyword-cluster-headless-cms-20260514/PLAN.md)

Keyword Cluster

Transform seed keywords into a prioritised cluster plan: each cluster grouped by search intent and theme, with volume totals, a pillar concept, spoke articles, and suggested H1/H2 for each spoke.

Prerequisites

• SE Ranking MCP server connected.
• User provides: (a) 3 to 20 seed keywords, (b) target market country (default: us), and optionally (c) minimum volume threshold (default: 100/mo), (d) maximum KD (default: 60).

Process

1. Expand seeds DATA_getRelatedKeywords, DATA_getSimilarKeywords, DATA_getLongTailKeywords
2. For each seed, pull related + similar + long-tail variants in the target country.
3. Target at least 100 candidate keywords per seed; de-duplicate across seeds.

1. Question-based expansion DATA_getKeywordQuestions
2. Pull question-intent keywords for the top 5 seeds.
3. These usually become spoke articles with PAA/featured-snippet potential.

1. Clean and filter
2. Remove keywords below min volume and above max KD.
3. Strip branded terms the target does not own.
4. Tag each keyword with detected intent: informational, commercial, transactional, navigational.

1. Cluster by SERP overlap DATA_getSerpResults (or DATA_getSerpTaskAdvancedResults)
2. Group keywords by how Google actually ranks them — shared top-10 organic URLs — not by text similarity. Token-overlap clustering manufactures cannibalisation; see references/serp-overlap-methodology.md for the full algorithm and anti-pattern callouts.
3. Budget guard before running. Compute estimated_credits = num_candidate_keywords × per_keyword_cost where per_keyword_cost = 3 (SERP-standard, default) or 10 (SERP-advanced, only if downstream needs AIO/PAA). Standard is sufficient for clustering. If estimated_credits > 500, surface the figure to the user and offer two paths: (a) proceed with SERP-standard, (b) trim the candidate set by raising the min-volume / lowering the max-KD thresholds in step 3 and re-running. If the user already requested SERP-advanced and the estimate exceeds 500, additionally offer SERP-standard as a cheaper fallback.
4. Fetch SERPs (one call per unique candidate keyword, cached for the session) — see references/serp-overlap-methodology.md § "Caching". Total SERP fetches = number of keywords, not number of pairs.
5. Pairwise overlap scoring. For each pair within an intent pre-group (see references/serp-overlap-methodology.md § "Pre-Grouping" for the optimisation that avoids full O(N²)), count shared URLs in the top 10 organic. Apply thresholds: 7-10 shared = same post (merge keywords), 4-6 = same cluster, 2-3 = interlink across clusters, 0-1 = separate clusters or exclude.
6. Form clusters from the connected components in the 4-6+ overlap graph. Target 5 to 12 clusters. Each cluster gets a name, primary keyword, secondary keywords, total volume, weighted KD.
7. Classify each cluster as pillar-worthy (broad, high volume, informational) or spoke-only (narrow, specific).

1. Pillar plus spokes architecture
2. For each pillar cluster, nominate 3 to 7 spoke articles (each one from a sub-cluster or question).
3. For each spoke, draft an H1 and 3 to 5 H2s.
4. Map internal-link structure: pillar links to all spokes, spokes link back to pillar, spokes cross-link where topically adjacent.

1. Prioritise
2. Applied after clusters are formed via SERP-overlap in step 4 — the formula scores already-grouped clusters, it does not influence which keywords cluster together.
3. Score each cluster: volume (40%) + inverse KD (30%) + commercial intent weighting (30%).
4. Output a prioritised build order.

1. Quality scorecard (post-synthesis validation)
2. After PLAN.md is written, run a 4-metric quality scorecard against the produced plan and warn the user if any metric fails. Inspired by theirs' post-execution scorecard model — adapted to our cluster-plan output (we score the plan, not generated content, since seo-keyword-cluster stops at the architecture).
3. Cannibalisation (zero tolerance). No two clusters in the plan should share ≥ 40% SERP overlap with each other (computed from the cached SERP matrix in step 4). If two clusters trip this gate, re-merge them and re-run from step 5 onward.
4. Orphan (zero tolerance). Every spoke article in the plan must be linked from its pillar in the internal-link map produced in step 5. Any spoke without an inbound link from its pillar is an orphan.
5. Coverage. The pillar page in each cluster must cover ≥ 70% of the cluster's high-volume keywords (top half of the cluster by volume) in its primary keyword + secondary keyword set, or via the H2s drafted in step 5. Below 70% means the pillar is too narrow for the cluster it heads.
6. Anchor diversity. Across all internal links inside a cluster (pillar↔spoke + spoke↔spoke), no single anchor text should be used > 40% of the time. Concentration above 40% is an over-optimisation signal.
7. Output. If all four metrics pass, append a single line to PLAN.md under "## Quality scorecard": All gates passed (cannibalisation/orphan/coverage/anchor-diversity). If any metric fails, append a "## Quality scorecard" section to PLAN.md with red/yellow/green rows for each metric (red = fail, yellow = within 10% of threshold, green = pass), and annotate the verdict header at the top of PLAN.md with (needs review — N quality-gate failures). Also write the same scorecard verbatim to 06-quality-scorecard.md in the output folder so it's auditable independently.

Output format

Create a folder seo-keyword-cluster-{target-slug}-{YYYYMMDD}/ with:

seo-keyword-cluster-{target-slug}-{YYYYMMDD}/
├── 01-seed-expansion.md
├── 02-filtered-keywords.md
├── 03-cluster-assignment.md      (SERP overlap matrix + cluster groupings)
├── 06-quality-scorecard.md       (evidence) — 4-metric gate result; written every run
├── keywords.csv
└── PLAN.md

PLAN.md follows this shape:

# Cluster Plan: {topic} {(needs review — N quality-gate failures) if step 7 flagged any}
Market: {country}
Seeds: {seed list}

## Summary
- Keywords analysed: {n}
- Clusters formed: {n}
- Estimated combined monthly volume: {n}
- Pillars: {n}, spokes: {n}
- Clustering method: SERP-overlap top-10 (mode: {standard | advanced}, ~{credits} credits)

## Build order

### Cluster 1: {cluster name} [PILLAR]
- Primary keyword: {kw} ({volume}/mo, KD {kd})
- Secondary: {list}
- Total volume: {n}/mo
- Priority score: {n}

#### Pillar page
- H1: {H1}
- H2s: {list}

#### Spoke articles
1. **{spoke title}**
   - H1: {H1}
   - H2s: {list}
   - Target keyword: {kw} ({volume})
2. **{spoke title}** ...

### Cluster 2: {cluster name} [SPOKE-ONLY]
...

## Internal linking map
- Pillar A links to: spokes A1, A2, A3
- Spoke A1 links back to: pillar A, and cross-links to spoke B2 (topical overlap)
...

## Quality scorecard
{If all four gates pass:}
All gates passed (cannibalisation/orphan/coverage/anchor-diversity).

{If any fail, render this table instead:}
| Gate | Status | Detail |
|---|---|---|
| Cannibalisation (no two clusters ≥40% SERP overlap) | RED / YELLOW / GREEN | {detail} |
| Orphan (every spoke linked from its pillar) | RED / YELLOW / GREEN | {detail} |
| Coverage (pillar covers ≥70% of cluster's high-volume keywords) | RED / YELLOW / GREEN | {detail} |
| Anchor diversity (no anchor used >40% of internal links per cluster) | RED / YELLOW / GREEN | {detail} |

## Raw data
- keywords.csv: full enriched keyword list
- 03-cluster-assignment.md: every keyword and its cluster (incl. SERP overlap matrix)
- 06-quality-scorecard.md: standalone copy of the scorecard above (evidence)

keywords.csv columns: keyword,volume,kd,cpc,intent,cluster,role_in_cluster

Tips

• Respect Data API rate limit: 10 requests per seco