skills/ai-seo/references/okf.md
Google's v0.1 markdown spec for representing site content as an agent-readable bundle. Introduced on the Google Cloud blog on 2026-06-12 and shipped inside Knowledge Catalog.
OKF is a directory of cross-linked markdown files. Each file has:
type required; title, description, resource, tags, timestamp recommended)An optional index.md lists the files for progressive disclosure. The bundle can be distributed as a git repo (recommended), a tarball/zip, or a subdirectory of a larger repo.
The full spec fits on one page. The repo lives under GoogleCloudPlatform (the "not an official Google product" disclaimer is Google's standard open-source boilerplate, not a denial — it appears on most of Google's open-source repos including their main AI samples repo).
---
type: Article
title: How to Connect the Ahrefs MCP Server to Manus
description: The official MCP servers, why they did not connect, and the fix.
resource: https://yoursite.com/blog/ahrefs-mcp-manus/
tags: [mcp, ahrefs]
---
# How to Connect the Ahrefs MCP Server to Manus
The body of the post, as clean markdown.
Add an index.md that lists all files so an agent can see the bundle's shape before opening each file, and that is the entire format.
Google built OKF for data teams sharing catalog metadata — BigQuery tables, API endpoints, metrics, playbooks. Most of the spec's examples are data-team artifacts, not blog posts. Google's blog post framing: "improve data sharing" and "standardized documentation" for collaboration across teams.
Pointing OKF at a marketing site is a clever repurposing popularized by Suganthan Mohanadasan. It's a legitimate use case for the format but not Google's primary one. Frame it accurately when explaining it to founders or marketing teams.
Nothing immediate. Nothing crawls the web for OKF bundles yet — the spec is weeks old, no AI engine has announced integration, and Knowledge Catalog ingests bundles only for paying enterprise customers' data teams.
Treat OKF as protocol-layer registration — the same shape of bet as early schema.org adoption was a decade ago. Schema took the better part of ten years to pay off; people who shipped it early are still glad they did.
A secondary benefit that pays off today regardless: generating the bundle is itself an internal-linking audit. Suganthan's tool draws every page as a node and every internal link as an edge, so islands and orphans become obvious at a glance.
| Layer | Purpose |
|---|---|
sitemap.xml | Tells a crawler which URLs exist |
robots.txt (with AI bot rules) | Permits or blocks AI crawlers |
llms.txt | Points an agent at the handful of pages you most want read |
/pricing.md | Structured pricing for agent-buyer comparisons |
/okf/ bundle | Hands over the content itself as cross-linked concepts |
| Schema markup | Per-page structured data (Article, FAQPage, Product, etc.) |
These stack rather than compete. llms.txt is a signpost, OKF is the library.
Three options, ordered by how much effort they take:
suganthan.com/okf-generator — paste a URL or sitemap, crawls up to 100 pages, returns a downloadable bundle. Also draws the resulting page graph so you can spot disconnected pages before publishing.
Suganthan's plugin (free, GPL, awaiting wp.org approval at time of writing) installs in a minute, serves the bundle at /okf/, and rebuilds on every publish or edit so it stays in sync. Direct download link is in his blog post. Requires WordPress 6.0+ and PHP 7.4+. Read-only — never edits posts or settings.
Only practical for a handful of pages. Each post becomes a markdown file with frontmatter that you cross-link manually. Miserable for a whole site.
Serve the bundle at yoursite.com/okf/, starting with yoursite.com/okf/index.md:
/okf/After it's serving, add a line to llms.txt pointing to the bundle so agents that read llms.txt (today) can discover the bundle (later).
llms.txt, schema markup, or other machine-readable files (OKF compounds with those; alone it does nothing)OKF is v0.1, weeks old. Worth tracking, not worth obsessing over:
okf/index.md to see who's shipping bundles