SEO for developer tools, docs, and APIs
Developers find tools through docs, GitHub, package registries, and more and more through coding assistants. I make sure your docs can be crawled, indexed, and read by all of them, and I tie the work back to signups and API keys.
How do you do SEO for a developer tool?
SEO for a developer tool starts with the docs: one canonical version of each page, API reference pages with enough explanation to rank, code samples that render without JavaScript, and changelog and integration pages that are linked and indexed. Then it covers how coding assistants and AI search describe the tool. Ravana Solutions provides SEO for developer tool companies across the US from Norfolk, Virginia. The work is done by its founder, Nick Mangubat, a developer who audits sites with Raptor, his own open source crawler written in Rust, and reports results in signups and API keys created.
Where SEO breaks for developer tools
Docs are usually the largest and most valuable part of a developer tool site, and the part with the least SEO attention.
Versioned docs that compete with themselves
Every release copies the docs into a new path. Without a clear canonical strategy, Google indexes old and current versions side by side, splits the links between them, and often ranks an outdated page.
Docs platforms with limited SEO control
Docusaurus, Mintlify, GitBook, and Nextra each make different choices about titles, canonicals, sitemaps, and rendering. Some settings are easy to change and some need a workaround. I check the output, not just the settings page.
Thin API reference pages
Generated reference pages list parameters and little else. They are hard to rank and easy for an assistant to skip. A short explanation, a working example, and links to the guides that use the endpoint make a real difference.
Changelogs and integration pages that never rank
Changelogs and integration pages describe exactly what developers search for, but they are often orphaned, badly paginated, or kept out of the index by accident.
Registries and GitHub as entity signals
Your npm, PyPI, or crates.io listing and your GitHub README are where search engines and assistants confirm what your tool is. When they describe it differently from your site, the picture gets blurry.
Code samples that need JavaScript to appear
Tabs, copy buttons, and client side syntax highlighting can hide code from any crawler that does not run scripts. The sample is often the most useful thing on the page.
What I do about it
Technical SEO
A versioning and canonical strategy, docs platform configuration, sitemaps, rendering checks, and internal links between guides, reference, and changelog.
Technical SEO service →AI search visibility
A prompt set built around the questions developers ask assistants, such as which library to use for a task, tracked monthly, plus markdown versions of key docs pages and an llms.txt file so coding assistants can read your docs cleanly.
AI search visibility service →Programmatic SEO
Integration and example libraries generated from your own data, with thresholds so every page carries real content.
Programmatic SEO service →Coding assistants are now part of the funnel
Developers ask ChatGPT, Claude, and the assistant inside their editor which library to use, then paste the code it suggests. That suggestion comes from what the model has read: your docs, your README, Stack Overflow answers, and posts that compare you with alternatives.
Markdown versions of docs pages and an llms.txt file make it easier for an agent that fetches your docs to get clean text. I have not measured a ranking effect from llms.txt in AI search, and I will not sell it as one. For docs, the point is simpler: an agent reading your page should get the code sample, not a loading spinner.
A typical first 90 days
Audit the docs estate
Crawl the marketing site, docs, reference, and changelog together. Map every version path and canonical, check what renders without JavaScript, and baseline how assistants answer the questions in your category.
Fix versioning and rendering
Canonicals and sitemaps settled for each version, docs platform settings corrected, reference pages enriched in priority order, and registry and README copy aligned with the site.
Connect docs to signups
Docs to signup paths instrumented, integration and changelog pages linked and indexed, markdown docs and llms.txt shipped, and the first report against the baseline.
What I report on
Built on my own tooling and research
I do not rent the audit. Raptor is the open source SEO crawler I wrote in Rust, with 128 checks covering rendering, indexation, structured data, and performance. My field report on generative engine optimization uses data from a live client, a regional veterinary practice, to show how ChatGPT and Google AI Mode choose the sources they cite.
Checks in Raptor, the open source crawler I wrote in Rust.
AI search engines on every prompt set: ChatGPT, Perplexity, Gemini, Claude, and Google, where I check both AI Overviews and AI Mode.
Senior person on your account. Me, from the first crawl to the monthly report.
What this is not
- Not a content mill. I write briefs and strategy; your team or a writer you choose writes the content.
- No guaranteed rankings or AI citations. I measure and report instead.
- No link schemes and no bought mentions.
Frequently asked questions
How do you do SEO for a developer tool?
SEO for a developer tool starts with the docs: one canonical version of each page, API reference pages with enough explanation to rank, code samples that render without JavaScript, and changelog and integration pages that are linked and indexed. Then it covers how coding assistants and AI search describe the tool. Ravana Solutions provides SEO for developer tool companies across the US from Norfolk, Virginia. The work is done by its founder, Nick Mangubat, a developer who audits sites with Raptor, his own open source crawler written in Rust, and reports results in signups and API keys created.
How do you handle canonicals for versioned documentation?
Usually one version, typically the latest stable release, is the canonical target for each page. Older versions either point their canonical at it or stay out of the index, and the sitemap lists only what should rank. The right setup depends on how many users still run old versions, so I check your traffic and support data before deciding.
Can you work with Docusaurus, Mintlify, GitBook, or Nextra?
Yes. I review the configuration and the generated output of each, not just the settings page. When a platform cannot do what is needed, I say so and suggest the smallest workaround, which is often a redirect rule or a build step rather than a migration.
Should our docs have an llms.txt file?
It is cheap to add and helps agents that fetch docs directly. I would not expect it to change rankings or AI search citations on its own, and I have not seen it do that in client work. Markdown versions of key pages and clean server rendered HTML matter more.
How do you measure SEO for a developer tool?
By what developers do after they land: signups, API keys created, installs, and the paths from docs pages to signup. Traffic and rankings are inputs. I set up the tracking first so every later change can be judged against it.
Start with a free 30 minute call
Bring your docs platform, your versioning setup, and the questions developers ask before they install. If there is a fit, a scoped proposal with a fixed price follows within 2 business days.