Files
AutoGPT/docs/integrations/dataforseo/related_keywords.md
Nicholas Tindle c1a1767034 feat(docs): Add block documentation auto-generation system (#11707)
- Add generate_block_docs.py script that introspects block code to
generate markdown
- Support manual content preservation via <!-- MANUAL: --> markers
- Add migrate_block_docs.py to preserve existing manual content from git
HEAD
- Add CI workflow (docs-block-sync.yml) to fail if docs drift from code
- Add Claude PR review workflow (docs-claude-review.yml) for doc changes
- Add manual LLM enhancement workflow (docs-enhance.yml)
- Add GitBook configuration (.gitbook.yaml, SUMMARY.md)
- Fix non-deterministic category ordering (categories is a set)
- Add comprehensive test suite (32 tests)
- Generate docs for 444 blocks with 66 preserved manual sections

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>

<!-- Clearly explain the need for these changes: -->

### Changes 🏗️

<!-- Concisely describe all of the changes made in this pull request:
-->

### Checklist 📋

#### For code changes:
- [x] I have clearly listed my changes in the PR description
- [x] I have made a test plan
- [x] I have tested my changes according to the test plan:
  <!-- Put your test plan here: -->
  - [x] Extensively test code generation for the docs pages



<!-- CURSOR_SUMMARY -->
---

> [!NOTE]
> Introduces an automated documentation pipeline for blocks and
integrates it into CI.
> 
> - Adds `scripts/generate_block_docs.py` (+ tests) to introspect blocks
and generate `docs/integrations/**`, preserving `<!-- MANUAL: -->`
sections
> - New CI workflows: **docs-block-sync** (fails if docs drift),
**docs-claude-review** (AI review for block/docs PRs), and
**docs-enhance** (optional LLM improvements)
> - Updates existing Claude workflows to use `CLAUDE_CODE_OAUTH_TOKEN`
instead of `ANTHROPIC_API_KEY`
> - Improves numerous block descriptions/typos and links across backend
blocks to standardize docs output
> - Commits initial generated docs including
`docs/integrations/README.md` and many provider/category pages
> 
> <sup>Written by [Cursor
Bugbot](https://cursor.com/dashboard?tab=bugbot) for commit
631e53e0f6. This will update automatically
on new commits. Configure
[here](https://cursor.com/dashboard?tab=bugbot).</sup>
<!-- /CURSOR_SUMMARY -->

---------

Co-authored-by: Claude Opus 4.5 <noreply@anthropic.com>
2026-01-19 07:03:19 +00:00

3.8 KiB

Dataforseo Related Keywords

Blocks for finding semantically related keywords using the DataForSEO Labs API.

What it is

Get related keywords from DataForSEO Labs Google API

How it works

This block uses the DataForSEO Labs Google Related Keywords API to find semantically related keywords based on a seed keyword. It returns keywords that share similar search intent or topic relevance.

The depth parameter controls the breadth of the search, with higher values returning exponentially more related keywords. Results include search metrics, competition data, and optional SERP/clickstream information.

Inputs

Input Description Type Required
keyword Seed keyword to find related keywords for str Yes
location_code Location code for targeting (e.g., 2840 for USA) int No
language_code Language code (e.g., 'en' for English) str No
include_seed_keyword Include the seed keyword in results bool No
include_serp_info Include SERP information bool No
include_clickstream_data Include clickstream metrics bool No
limit Maximum number of results (up to 3000) int No
depth Keyword search depth (0-4). Controls the number of returned keywords: 0=1 keyword, 1=~8 keywords, 2=~72 keywords, 3=~584 keywords, 4=~4680 keywords int No

Outputs

Output Description Type
error Error message if the operation failed str
related_keywords List of related keywords with metrics List[RelatedKeyword]
related_keyword A related keyword with metrics RelatedKeyword
total_count Total number of related keywords returned int
seed_keyword The seed keyword used for the query str

Possible use case

Topic Clustering: Group related keywords to build comprehensive content clusters around a topic.

Semantic SEO: Discover LSI (latent semantic indexing) keywords to improve content relevance.

Keyword Expansion: Expand targeting beyond exact match to capture related search traffic.


What it is

Extract individual fields from a RelatedKeyword object

How it works

This block extracts individual fields from a RelatedKeyword object returned by the Related Keywords block. It separates the compound object into distinct outputs for workflow integration.

Outputs include the keyword text, search volume, competition score, CPC, keyword difficulty, and any SERP or clickstream data that was requested in the original search.

Inputs

Input Description Type Required
related_keyword The related keyword object to extract fields from RelatedKeyword Yes

Outputs

Output Description Type
error Error message if the operation failed str
keyword The related keyword str
search_volume Monthly search volume int
competition Competition level (0-1) float
cpc Cost per click in USD float
keyword_difficulty Keyword difficulty score int
serp_info SERP data for the keyword Dict[str, Any]
clickstream_data Clickstream data metrics Dict[str, Any]

Possible use case

Keyword Prioritization: Extract metrics to rank related keywords by opportunity score.

Content Optimization: Access keyword difficulty and search volume for content planning decisions.

Competitive Analysis: Pull competition and CPC data to assess keyword viability.