LSD
APIEndpoint 14 of 40Keyword Research / Semantic Clustering / Topic Expansion

Related Keywords API

The semantic keyword clustering API for the new local SEO stack.

Keyword Suggestions expands one seed into autocomplete variants. This is different: Related Keywords finds the broader topic cluster — keywords in the same semantic space, different phrasings, different angles, different intents but same subject matter. Ahrefs bundles this as 'Related Terms' inside Keywords Explorer ($129/mo), Semrush packages it as 'Related Keywords' in Keyword Magic Tool ($139.95/mo). We expose it as a per-call API. One seed keyword, 50-1000 semantically related terms with volume and competition. 2 credits ($0.01) per call. Your agent clusters content and prevents cannibalization.

Start Free — 50 CreditsView Docs

POST /v1/keywords/related · 2 credits / call

POST /v1/keywords/related5 of 80
Semantically related
KeywordVolumeCPCTrend
plumber near me12,100$45.20
emergency plumber austin2,400$38.50
best plumber austin tx880$22.10
pipe repair cost590$12.40
drain cleaning austin720$18.90
▌ Ask your agent

These prompts are the new semantic keyword clustering workflow.

Connect Local SEO Data as an MCP server once (60 seconds, below). Then your agent discovers related keywords, groups them by intent, and detects cannibalization risk. Replace bracketed keywords with your own.

Topic cluster discovery

Find related keywords for [best plumber] in [Denver]. Group by semantic intent. Which related terms have highest volume that I could target on different pages without cannibalizing?

Cannibalization prevention

Pull related keywords for [emergency plumber denver]. Flag any related terms with >100 search volume. These might compete with my existing content.

Content silo architecture

Expand [roof repair] for related keywords. Show me semantic clusters: repair vs replacement vs installation vs maintenance. Design pages for each cluster, one page per intent.

Service expansion mapping for agencies

Pull related keywords for [my client's primary service]. Map to adjacent services they could expand into. What related keyword clusters indicate untapped revenue?

▌ Real response

What you get back

Live response for 'emergency plumber' seed in Denver. Shows semantic variants in the same topic cluster. Real API response, May 2026.

response · application/json~2-4s · 2 credits
{
  "status": "success",
  "credits_used": 2,
  "data": {
    "seed_keyword": "emergency plumber",
    "location": "Denver, Colorado, United States",
    "total_results": 156,
    "related_keywords": [
      {
        "keyword": "24 hour plumber",
        "search_volume": 580,
        "cpc": 58.00,
        "competition": 0.89,
        "semantic_cluster": "availability"
      },
      {
        "keyword": "emergency plumbing service",
        "search_volume": 310,
        "cpc": 54.00,
        "competition": 0.84,
        "semantic_cluster": "service_variant"
      },
      {
        "keyword": "burst pipe emergency",
        "search_volume": 185,
        "cpc": 28.50,
        "competition": 0.61,
        "semantic_cluster": "problem_specific"
      },
      {
        "keyword": "after hours plumber",
        "search_volume": 220,
        "cpc": 52.25,
        "competition": 0.78,
        "semantic_cluster": "availability"
      },
      {
        "keyword": "plumbing emergency repair",
        "search_volume": 145,
        "cpc": 45.75,
        "competition": 0.72,
        "semantic_cluster": "service_variant"
      },
      {
        "keyword": "emergency water damage plumber",
        "search_volume": 95,
        "cpc": 41.00,
        "competition": 0.58,
        "semantic_cluster": "problem_specific"
      }
    ]
  }
}
▌ Returns

Everything your agent needs to cluster topics and prevent cannibalization

Semantic grouping

Keywords grouped by topic cluster and intent variant

Each related keyword is tagged with its semantic cluster (e.g., 'availability', 'service_variant', 'problem_specific'). Helps your agent see which keywords belong on the same page (same cluster) vs different pages (different clusters). This prevents content cannibalization — you don't want two pages competing for the same search intent.

Volume + competition per keyword

Search volume, CPC, competition index

For each related keyword: monthly search volume, cost-per-click in USD, and competition index (0-1). Your agent ranks by volume within each cluster. 'In the availability cluster, 24-hour-plumber has 580 volume vs after-hours-plumber at 220 — target the higher volume term first.'

Geographic specificity

All results are location-scoped

Related keywords are specific to your location. 'Emergency plumber' in Denver pulls different semantic variants and volumes than the same seed in Austin. Multi-location operators batch-query by city to build location-specific topical architectures.

Pair with Keyword Suggestions and Search Volume

Compose discovery, expansion, and validation

Keyword Suggestions expands one seed into autocomplete variants (same keyword type, different modifiers). Related Keywords finds semantic variants (different angles, different intents, same topic). Use both: Suggestions for long-tail discovery, Related Keywords for topical authority and content silo mapping. One agent, two endpoints, complete keyword intelligence.

▌ Built for

What SEO operators ship with this endpoint

Content silo architecture for agencies

Agencies pull related keywords for each primary service keyword, map semantic clusters, and build content siloes. 'Roof repair' has clusters for material type, problem type, and geographic intent. One page per cluster, parent page for the umbrella keyword. Prevents cannibalization, improves topical authority, ranks faster.

→ For agencies

Cannibalization audits for in-house teams

Pull related keywords for your top 20 target keywords. Map them against your existing content. Which pages are competing for the same semantic clusters? Consolidate or redesign to own clusters instead of scattered rankings across weak pages.

→ For in-house teams

Multi-service business expansion mapping

Franchises and service businesses pull related keywords for their primary service. Identify semantic clusters for adjacent services. 'If I rank well for roof repair, which related keyword clusters suggest I could also target roof installation or maintenance contracts?'

→ For franchises

Competitive topical authority comparison

Pull related keywords for your target keyword and your competitors' websites. Compare cluster coverage. Are they dominating specific semantic clusters you're absent from? Allocate content budget to underserved clusters.

→ For consultants
▌ vs. the alternatives

Why not use Ahrefs, Semrush, or standalone semantic clustering tools?

All of them offer keyword clustering, but the business model limits automation. Here's why the API model wins for agents.

ToolRelated keywords featureClustering approachCost modelAgent-ready
Ahrefs Keywords Explorer ($129/mo)Related Terms (dashboard view)SERP-based clustering$129–299/mo per seat, $1,548–3,588/yearAPI exists but secondary focus
Semrush Keyword Magic Tool ($139.95/mo)Related Keywords (export view)Semantic + volume-based$139.95–499.95/mo per seat, $1,679–5,999/yearCSV export, not agent-native
Keyword Cupid (standalone)Semantic Keyword ClusteringML-trained semantic groupingPer-report pricing, higher per-query costNo MCP, report-oriented
Topical Map (standalone)Topic cluster mappingSemantic topic modelingSubscription + per-cluster feesAPI available but limited
SE Ranking ($65/mo)Keyword clustering (included)Basic semantic grouping$65–159/mo per seatAPI available on higher plans
LocalSEOData Related Keywords APIFull semantic cluster taggingIntent-based + semantic grouping$0.01 per call, no seatsNative MCP + REST, agent-first
▌ Connect in 60 seconds

Use it from your agent

Two integration surfaces: MCP for clients that speak MCP, REST API for everything else.

Direct MCP integration

Drop-in support in Claude Desktop, OpenClaw, Hermes Agent, and any MCP-aware client.

Add to your client's MCP config (e.g. claude_desktop_config.json):

{
  "mcpServers": {
    "localseodata": {
      "url": "https://mcp.localseodata.com",
      "headers": {
        "Authorization": "Bearer sk_live_..."
      }
    }
  }
}

REST API

For Perplexity Computer, ChatGPT Custom GPTs, custom agents, and any platform that calls REST endpoints directly.

Base URL:

api.localseodata.com

See the docs for endpoint reference and auth.

▌ Quickstart

Your first call in three lines

Same endpoint, three syntaxes. The `keywords` array accepts 1-20 seed keywords. `location` is required in 'City, State, Country' format. Optional: `limit` (default 50, max 1000), `language` (default 'en'). One call = 2 credits, regardless of how many related keywords are returned.

terminal · curl
POST /v1/keywords/related
curl -X POST https://api.localseodata.com/v1/keywords/related \
  -H "Authorization: Bearer sk_live_..." \
  -H "Content-Type: application/json" \
  -d '{
    "keywords": ["emergency plumber"],
    "location": "Denver, Colorado, United States",
    "limit": 100
  }'
Full endpoint reference → · Get an API key →
▌ Pricing for this endpoint

$0.01 per call

2 credits per search. Free tier on signup includes 50 credits (25 related keyword searches). Monthly plans start at $19 and never expire. No per-seat fees, no team limits.

Free tier
25
related keyword searches on signup (50 credits)
Starter · $19/mo
1,900
searches/mo at this rate
Per-call cost
$0.01
or 2 credits per search
▌ FAQ

Common questions

What is the Related Keywords API?+
A REST endpoint that takes 1-20 seed keywords and returns 50-1,000 semantically related keywords in the same topic cluster. Each result includes monthly search volume, cost-per-click in USD, competition index (0-1), and semantic cluster tag (availability, intent variant, problem-specific, etc.). The endpoint is POST /v1/keywords/related. One call returns structured JSON and costs 2 credits ($0.01). This is the foundational endpoint for content silo architecture and cannibalization prevention — mapping which keywords belong on the same page vs different pages in your topical structure.
How is this different from Keyword Suggestions API?+
Keyword Suggestions API takes one seed keyword and returns long-tail autocomplete variants — the same kind of term, expanded. Examples: 'emergency plumber' expands to 'emergency plumber near me', 'emergency plumber denver', '24 hour emergency plumber', 'how much does emergency plumbing cost'. These are variations of the same search intent. Related Keywords API returns semantically related keywords in the same topic cluster — different phrasings, different angles, potentially different intents, but same subject matter. Examples: 'emergency plumber' relates to 'burst pipe emergency', 'water damage plumber', 'pipe repair service', '24 hour plumber'. Suggestions are for long-tail discovery and targeting more of the same intent. Related Keywords are for understanding the broader topic landscape and building content silos that own multiple intent angles around a core topic.
What are semantic clusters?+
Semantic clusters are groups of keywords that share meaning and intent, even if the surface-level wording is different. Example: 'emergency plumber', '24 hour plumber', 'after hours plumber', and 'plumber on call' all address the same user intent (urgent plumbing help available now) — even though the words are different. The Related Keywords API tags each result with its cluster (e.g., 'availability', 'service_variant', 'problem_specific'). This helps your agent group keywords by true intent, not just text matching. Content silo strategy: one page per cluster, targeting the highest-volume keyword in that cluster as the page title, and the lower-volume variants as supporting content and internal links.
Why do I need both Suggestions and Related Keywords?+
Suggestions and Related Keywords solve different problems. Use Suggestions to discover long-tail variants of the same keyword — 'roof repair' → 'roof repair cost', 'roof repair near me', 'roof repair financing', etc. These are all people searching for roof repair service specifically. Use Related Keywords to understand the broader topic — 'roof repair' → 'roof replacement', 'roof maintenance', 'roof inspection', 'shingle replacement'. These are related but different services. Suggestions feed your content strategy for long-tail rankings. Related Keywords feed your topical authority strategy — helping you decide 'I should own the roof service cluster, not just roof repair.' A typical workflow: Related Keywords first (understand the topic landscape), then Suggestions on each cluster keyword (find the long-tail variants).
How does this compare to Ahrefs ($129/mo)?+
Ahrefs Keywords Explorer includes a 'Related Terms' feature in the dashboard. You search a keyword, and it shows you related keywords with volume and difficulty. But it's dashboard-first, per-seat subscription, and the API is secondary. With Related Keywords API, you get the same data structure as an endpoint. Batch-query 10+ seeds in a single agent prompt, get structured JSON back, filter by cluster and volume, and map content architecture automatically. For a consultant doing occasional keyword research, Ahrefs dashboard is fine. For an agency building content strategies across 20 clients, the API is faster and cheaper: $0.01 per search vs $129/mo flat fee.
How does this compare to Semrush ($139.95/mo)?+
Semrush Keyword Magic Tool includes 'Related Keywords' as a data column in exports. You can export related keywords alongside volume and difficulty. But the workflow is dashboard-driven (click, export, analyze in spreadsheet), and you pay per-seat. With Related Keywords API, the data is structured, semantic-cluster-tagged, and ready for agents to analyze and act on. Semrush requires a human to review the export and decide what to do with it. The API lets your agent group keywords by cluster, detect cannibalization risk, and recommend content silo architecture in a single prompt. For interactive exploration, Semrush is fine. For automation and scale, the API wins.
What upstream data sources do you use?+
Related Keywords data comes from DataForSEO, our upstream provider. DataForSEO aggregates search volume from Google's historical metrics (Google Ads API), Bing clickstream data, and proprietary clickstream sources. Semantic clustering is done by analyzing SERP overlap and query intent signals from Google's search results. Most competitive tools (Ahrefs, Semrush, SE Ranking) use the same upstream data; the difference is we're API-first and pass cost transparently to you instead of bundling it into a per-seat subscription.
Can I look up multiple seed keywords in one call?+
Yes. The keywords array accepts 1-20 seed keywords. If you search for ['emergency plumber', 'water heater repair', 'drain cleaning'], you get related keywords for all three seeds, combined and deduplicated. Cost is still 2 credits for the call. This is useful for mapping related keywords across your entire service offering in one batch.
How does the competition index work?+
Competition (0-1 scale) reflects how many high-authority domains are competing for that keyword. 0-0.3 = low competition (easy to rank), 0.3-0.7 = medium (moderate work), 0.7-1.0 = high (lots of entrenched competitors). For content silo strategy, target high-volume related keywords in clusters where competition is <0.7. Pair with Keyword Opportunities to rank opportunities by difficulty vs your current rank.
Can AI agents use this directly?+
Yes, two ways. MCP: Add Local SEO Data to your claude_desktop_config.json (or any MCP-compatible client), and Claude calls this endpoint from your prompt. You say 'find related keywords for my primary service and map semantic clusters' and it happens. REST: Any agent that can make HTTPS calls hits api.localseodata.com with Authorization: Bearer sk_live_.... The agent receives structured JSON with cluster tags and can group results, detect cannibalization, and recommend content silo architecture automatically.
Do you support multi-language and multi-location searches?+
location is required and must be in 'City, State, Country' format (e.g., 'Denver, Colorado, United States'). Related keywords are location-specific — the semantic variants you get for 'plumber' in Denver differ from 'plumber' in Austin because intent and available services differ by market. language is optional (default 'en') and accepts language codes. Multi-location operators batch-query by city to build location-specific content silos. Multi-language is fine too — the API returns related keywords in the specified language.
What changed in 2026 that made this category exist?+
Two things. First, MCP (the Model Context Protocol) became standard and agents became the primary interface to SEO tools. Before MCP, you logged into Ahrefs or Semrush dashboards and clicked around. Now agents do keyword research automatically. The moment agents took over, the business model changed — you need an API-first tool designed for batch automation, not a dashboard designed for humans. Second, content silo strategy and topical authority became the dominant SEO framework. Google now rewards deep coverage of topic clusters over scattered keyword rankings. Ahrefs and Semrush are still dashboard-first tools optimized for single-keyword exploration. The Related Keywords API is built for the new workflow: agent discovers related keywords, maps semantic clusters, recommends topical silo architecture, and your team executes.
▌ Related endpoints

Often used in the same agent prompt

POST /v1/keywords/suggestions
Keyword Suggestions API

Expand seeds into long-tail autocomplete variants.

POST /v1/keywords/search-volume
Search Volume API

Validate keywords with volume, CPC, and monthly trends.

POST /v1/keywords/opportunities
Keyword Opportunities API

Rank keywords by opportunity score and difficulty.

POST /v1/keywords/site-keywords
Keywords for Site API

What keywords competitors rank for.

Map your topic clusters before you start writing.

50 free credits on signup. 25 related keyword searches included. Discover semantic variants and prevent cannibalization in your first agent prompt.

Start FreeRead the docs
▌ MADE FOR THE NEW LOCAL SEO STACK