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DataForSEO Integration Guide

Overview

DataForSEO provides real keyword data, competitor intelligence, and AI search optimization metrics. This replaces guesswork with data-driven decisions.

MCP Access: DataForSEO tools are available through MCP. Use them directly in your research workflow.

Budget Protocol

Per session limit: $0.50 (unless user explicitly approves more)
Monthly budget: ~$10
Always report: Show user what API calls you're making and estimated cost

Core Principle

Start with seeds → expand with related → filter by opportunity → verify with SERP.

Don't chase high-volume competitive keywords. Find gaps where we can win.

For @spy: Competitive Intelligence

Competitor Keywords

Tool: dataforseo_labs_google_ranked_keywords
Use: See what keywords competitors rank for
Target: fal.ai, replicate.com, runware.ai, cloudinary.com

Backlink Analysis

Tool: backlinks_summary, backlinks_referring_domains
Use: Where competitors get links, potential outreach targets

Domain Intersection

Tool: dataforseo_labs_google_domain_intersection
Use: Find keywords multiple competitors rank for (validated demand)

LLM Mentions (GEO)

Tool: ai_optimization_llm_mentions_search
Use: Check if Banatie or competitors mentioned in AI responses
Platform: chat_gpt, google (AI Overview)

For @strategist: Keyword Research

Search Volume

Tool: keywords_data_google_ads_search_volume
Use: Get real monthly search volume for keyword list
Input: Up to 1000 keywords per request

Keyword Difficulty

Tool: dataforseo_labs_bulk_keyword_difficulty
Use: Score 0-100, lower = easier to rank
Filter: KD < 50 for realistic targets

Tool: dataforseo_labs_google_related_keywords
Use: Expand seed keywords, find long-tail opportunities
Depth: 1-4 (start with 1, go deeper if needed)

Search Intent

Tool: dataforseo_labs_search_intent
Use: Classify keywords as informational/navigational/commercial/transactional
Match: Content type should match intent

AI Search Volume (GEO Priority)

Tool: ai_optimization_keyword_data_search_volume
Use: Keywords popular in AI search (ChatGPT, Perplexity)
Why: Early indicator of emerging queries

Research Workflow

Start with seeds (3-5 per topic)
Get search volume for seeds
Expand top 3 by volume with related keywords
Filter: Volume > 50, KD < 50
Check intent for finalists
SERP analysis for top candidates

For @seo: Optimization & Verification

SERP Analysis

Tool: serp_organic_live_advanced
Use: See current top 10 results, SERP features present
Check: Featured snippets, PAA, video results

On-Page Analysis

Tool: on_page_instant_pages
Use: Technical SEO check of specific URL
After: Publishing, verify optimization

LLM Responses (GEO)

Tool: ai_optimization_llm_response
Use: See how AI models answer our target queries
Why: Optimize content for AI citations

Key Learnings

Problem-aware keywords often have zero volume. People search for solutions, not problems. "placeholder images slow" = 0 volume. "generate images api" = real volume.

Related keywords > seed keywords. Your initial guesses are rarely the best targets. Let data guide expansion.

Brand keywords are useless. "cloudinary pricing" means they already chose Cloudinary. Target problem/solution queries.

Low KD + decent volume = opportunity. Don't chase "ai image generation" (KD 80+). Find "generate images for nextjs" (KD 30, volume 200).

Output Format

When reporting keyword research:

## Keyword Research: [Topic]

### Seeds Analyzed
| Keyword | Volume | KD | Intent |
|---------|--------|----|----|
| ... | ... | ... | ... |

### Top Opportunities
| Keyword | Volume | KD | Rationale |
|---------|--------|----|----|
| ... | ... | ... | Why this is a good target |

### Recommendations
[What content to create based on this data]

### API Calls Made
[List of tools used, estimated cost]

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