KWI Content Calendar MCP Server

An MCP server that turns a Keyword Insights clustering CSV into a prioritised, week by week content calendar. Works with Claude Desktop, Claude Code, ChatGPT, Windsurf, Cursor, Manus AI, and any MCP-compatible tool. Your AI handles the strategic analysis. The server handles CSV parsing, cluster enrichment, and a formatted Excel export with 5 sheets.

Content Calendar sheet with 19 columns, colour-coded actions, and a TOTALS row

What it does

#	Tool	Description
1	parse_clustering_csv	Reads a KWI clustering CSV, validates 18 required columns, compacts to top N clusters by opportunity, enriches with derived columns (mismatch counts, hub sizes, supporting keywords), and returns data with the full analysis instructions for the AI
2	export_content_calendar	Takes the calendar JSON that your AI generates and writes a formatted Excel workbook (5 sheets) plus a flat CSV to disk

What the AI receives

When parse_clustering_csv runs, it returns:

Cluster data. Lead-keyword rows only, sorted by opportunity, capped at max_clusters (default 500). Each row includes cluster metrics, intent, funnel stage, hub/spoke taxonomy, and 4 derived columns: _ranking_kw_count, _mismatch_count, _supporting_kws, _hub_cluster_count
Analysis instructions. A complete decision framework covering: action classification (CREATE/UPDATE/OPTIMISE/REWRITE/MAINTAIN/DEPRIORITISE), composite priority scoring (6 weighted factors plus multipliers), content type recommendation from SERP data, and calendar scheduling rules (balanced mix, funnel targets, URL consolidation, weekly sequencing)
User configuration. Domain, pieces per week, weeks, and TOFU/MOFU/BOFU target percentages

Parse CSV stats output

What the Excel export contains

5 formatted sheets:

Sheet	Description
Content Calendar	Week by week plan with 19 columns: week, score, action, target keyword, content type, cluster metrics, cluster URL, needs-scraping flag, intent, journey, hub topic, supporting keywords, SERP features, AI Overview flag, and action rationale. Colour-coded actions, priority colour scale, frozen header, TOTALS row
Gap Analysis Logic	Documentation sheet. A 4-step playbook for what to scrape when an action is UPDATE/OPTIMISE/REWRITE: existing page signals, competitor scraping, gap calculation, and enhanced brief generation
Pages to Scrape	Filtered to UPDATE/OPTIMISE/REWRITE items with URLs, cluster avg rank, content score, keyword count, and a concrete "What to Look For" instruction per page
Priority Backlog	Next 30 clusters not in the calendar, ranked by priority score, including Hub column so you can see topical clustering
Summary	Two sections. First: six "Cluster-Level Analysis Approach" principles explaining how the calendar was built. Second: Calendar Metrics including total clusters analysed, scheduled items, items needing page scraping, total cluster search volume, opportunity, keywords covered, and average priority score

Sample output

The Action Rationale column writes out intent mismatches in plain English so the reason each CREATE was scheduled is visible at a glance:

Intent mismatch reasons in Action Rationale column

Gap Analysis Logic documentation sheet, a 4-step playbook for the scraping pass that feeds the writing brief:

Gap Analysis Logic sheet

Summary sheet with the Cluster-Level Analysis Approach principles table and the Calendar Metrics block:

Summary sheet

Priority Backlog with Hub column for topical clustering context:

Priority Backlog sheet

Setup

1. Clone and build

hljs language-bash

git clone https://github.com/Suganthan-Mohanadasan/kwi-content-calendar-mcp.git
cd kwi-content-calendar-mcp
npm install
npm run build

2. Configure your AI tool

Claude Desktop (~/Library/Application Support/Claude/claude_desktop_config.json):

hljs language-json

{
  "mcpServers": {
    "kwi-content-calendar": {
      "command": "node",
      "args": ["/path/to/kwi-content-calendar-mcp/dist/index.js"]
    }
  }
}

Claude Code (~/.claude.json under mcpServers):

hljs language-json

{
  "kwi-content-calendar": {
    "type": "stdio",
    "command": "node",
    "args": ["/path/to/kwi-content-calendar-mcp/dist/index.js"]
  }
}

ChatGPT Desktop / Windsurf / Cursor: Same pattern. Point your MCP config at dist/index.js using stdio transport.

Restart your AI tool after adding the config. Once loaded, the two tools appear in the MCP server list:

Claude Code showing kwi-content-calendar MCP server with parse_clustering_csv and export_content_calendar tools

Parameters

parse_clustering_csv

Parameter	Required	Default	Description
`csv_path`	Yes		Absolute path to the KWI clustering CSV
`domain`	Yes		Client domain (e.g. example.com)
`pieces_per_week`	No	3	Content pieces per week
`weeks`	No	4	Calendar duration in weeks
`tofu_pct`	No	45	TOFU (awareness) target percentage
`mofu_pct`	No	30	MOFU (consideration) target percentage
`bofu_pct`	No	25	BOFU (conversion) target percentage
`max_clusters`	No	500	Top N clusters by opportunity to analyse

export_content_calendar

Parameter	Required	Default	Description
`calendar_json`	Yes		JSON string with `calendar` and `backlog` arrays
`output_dir`	Yes		Directory to save output files
`filename_prefix`	No	content_calendar	Prefix for output filenames
`total_clusters`	No	0	Unique clusters from the source CSV (for Summary sheet)
`domain`	No	""	Client domain for the Content Calendar subtitle
`pieces_per_week`	No	3	Pieces per week used in generation
`weeks`	No	4	Calendar duration in weeks

The AI is instructed by the analysis prompt to pass the metadata params through from the parse_clustering_csv stats, so the Content Calendar title and Summary sheet render correctly.

Export confirmation in Claude Desktop

Usage

Just talk to your AI:

"Parse the clustering CSV at ~/Downloads/cluster_results.csv for domain example.com"
"Analyse these clusters and build a content calendar, 3 pieces per week for 6 weeks"
"Focus more on BOFU content: 20% TOFU, 30% MOFU, 50% BOFU"
"Export the calendar to ~/Desktop/"
"Show me just the week 1 priorities"
"Which clusters have intent mismatch issues?"
"What's in the backlog?"

How it works

You provide a KWI clustering CSV (exported from Keyword Insights)
The server parses, validates, and compacts the CSV. 5,000 rows become ~500 enriched lead-keyword rows, roughly 33,000 tokens
Your AI analyses the data using the included decision framework
The AI produces a JSON calendar with prioritised, sequenced content items
The server exports to formatted Excel plus CSV

The AI handles strategy. The server handles I/O.

The scoring model

Every cluster is scored from 0 to 100 using 6 weighted factors plus multipliers for SERP features:

Factor	Weight	Why it matters
Opportunity	25%	Estimated additional traffic if rankings improve
Difficulty	20%	Lower competition means faster results
Rank proximity	20%	Closer to page 1 means less effort needed
Intent alignment	15%	Mismatches get urgency points
Funnel stage	10%	BOFU scores highest, because BOFU converts
Hub size	10%	Clusters in large topic hubs build topical authority

Multipliers: AI Overview in SERP features applies x1.15. Featured snippet applies x1.10. Clusters with 5 or more keywords apply x1.10. Zero search volume applies x0.70.

The action decision tree

Each cluster gets classified before it lands in the calendar or the backlog:

CREATE (mismatch) if >60% of ranking keywords have the wrong page type
MAINTAIN if cluster avg rank is top 5 (skipped from the calendar, lives in the backlog)
OPTIMISE if cluster avg rank is 6 to 15
UPDATE if cluster avg rank is 16 to 30, or 31 to 60 with a content score above 0.4
REWRITE if cluster avg rank is 31 to 60 with a content score under 0.4, or 61 plus
CREATE (quick win / strategic / gap fill) if not ranking and opportunity or difficulty conditions fire
DEPRIORITISE if not ranking, low opportunity, high difficulty

Week 1 is reserved for intent mismatches. Week 2 for striking distance updates. Week 3 mixes quick wins with page 1 optimisations. Week 4 onwards picks up balanced fill with journey stage constraints applied.

Getting a clustering CSV

Sign up at keywordinsights.ai
Create a clustering project with your keyword list
Run the clustering
Export results as CSV

The export includes all required columns: cluster metrics, intent classifications, funnel stages, hub/spoke taxonomy, SERP features, and ranking data.

Blog post

Full walkthrough with screenshots and use cases: How I Turn 5,000 Keywords Into a Content Calendar in 3 Minutes (with Claude and an MCP Server)

License

MIT

kwi-content-calendar-mcp