- name
- seo-dataforseo
- description
- SEO keyword research using the DataForSEO API. Perform keyword analysis, YouTube keyword research, competitor analysis, SERP analysis, and trend tracking. Use when the user asks to: research keywords, analyze search volume/CPC/competition, find keyword suggestions, check keyword difficulty, analyze competitors, get trending topics, do YouTube SEO research, or optimize landing page keywords. Requires a DataForSEO API account and credentials in .env file.
SEO Keyword Research (DataForSEO)
Setup
Install dependencies:
pip install -r scripts/requirements.txtConfigure credentials by creating a .env file in the project root:
DATAFORSEO_LOGIN=your_email@example.com
DATAFORSEO_PASSWORD=your_api_passwordGet credentials from: https://app.dataforseo.com/api-access
Quick Start
| User says | Function to call |
|---|---|
| "Research keywords for [topic]" | keyword_research("topic") |
| "YouTube keyword data for [idea]" | youtube_keyword_research("idea") |
| "Analyze competitor [domain.com]" | competitor_analysis("domain.com") |
| "What's trending?" | trending_topics() |
| "Keyword analysis for [list]" | full_keyword_analysis(["kw1", "kw2"]) |
| "Landing page keywords for [topic]" | landing_page_keyword_research(["kw1"], "competitor.com") |
Execute functions by importing from scripts/main.py:
import sys
from pathlib import Path
sys.path.insert(0, str(Path("scripts")))
from main import *
result = keyword_research("AI website builders")Workflow Pattern
Every research task follows three phases:
1. Research
Run API functions. Each function call hits the DataForSEO API and returns structured data.
2. Auto-Save
All results automatically save as timestamped JSON files to results/{category}/. File naming pattern: YYYYMMDD_HHMMSS__operation__keyword__extra_info.json
3. Summarize
After research, read the saved JSON files and create a markdown summary in results/summary/ with data tables, ranked opportunities, and strategic recommendations.
High-Level Functions
These are the primary functions in scripts/main.py. Each orchestrates multiple API calls for a complete research workflow.
| Function | Purpose | What it gathers |
|---|---|---|
keyword_research(keyword) | Single keyword deep-dive | Overview, suggestions, related keywords, difficulty |
youtube_keyword_research(keyword) | YouTube content research | Overview, suggestions, YouTube SERP rankings, YouTube trends |
landing_page_keyword_research(keywords, competitor_domain) | Landing page SEO | Overview, intent, difficulty, SERP analysis, competitor keywords |
full_keyword_analysis(keywords) | Strategic content planning | Overview, difficulty, intent, keyword ideas, historical volume, Google Trends |
competitor_analysis(domain, keywords) | Competitor intelligence | Domain keywords, Google Ads keywords, competitor domains |
trending_topics(location_name) | Current trends | Currently trending searches |
Parameters
All functions accept an optional location_name parameter (default: "United States"). Most functions also have boolean flags to skip specific sub-analyses (e.g., include_suggestions=False).
Individual API Functions
For granular control, import specific functions from the API modules. See references/api-reference.md for the complete list of 25 API functions with parameters, limits, and examples.
Results Storage
Results auto-save to results/ with this structure:
results/
├── keywords_data/ # Search volume, CPC, competition
├── labs/ # Suggestions, difficulty, intent
├── serp/ # Google/YouTube rankings
├── trends/ # Google Trends data
└── summary/ # Human-readable markdown summariesManaging Results
from core.storage import list_results, load_result, get_latest_result
# List recent results
files = list_results(category="labs", limit=10)
# Load a specific result
data = load_result(files[0])
# Get most recent result for an operation
latest = get_latest_result(category="labs", operation="keyword_suggestions")Utility Functions
from main import get_recent_results, load_latest
# List recent files across all categories
files = get_recent_results(limit=10)
# Load latest result for a category
data = load_latest("labs", "keyword_suggestions")Creating Summaries
After running research, create a markdown summary document in results/summary/. Include:
- Data tables with volumes, CPC, competition, difficulty
- Ranked lists of opportunities (sorted by volume or opportunity score)
- SERP analysis showing what currently ranks
- Recommendations for content strategy, titles, tags
Name the summary file descriptively (e.g., results/summary/ai-tools-keyword-research.md).
Tips
- Be specific — "Get keyword suggestions for 'AI website builders'" works better than "research AI stuff"
- Request summaries — Always create a summary document after research, named specifically
- Batch related keywords — Pass multiple related keywords at once for comparison
- Specify the goal — "for a YouTube video" vs "for a landing page" changes which data matters most
- Ask for competition analysis — "Show me what videos are ranking" helps identify content gaps
Defaults
- Location: United States (code 2840)
- Language: English
- API Limits: 700 keywords for volume/overview, 1000 for difficulty/intent, 5 for trends, 200 for keyword ideas