Toadstone
A free, self-hosted SEO crawler with an MCP server for AI agents
What is Toadstone?
Toadstone is a free, self-hosted SEO crawler for agencies, in-house SEOs and developers. It runs 222 checks with evidence and a one-line fix for each, joins Google Search Console and GA4 data to your crawl, and exports to Excel, CSV and HTML or Markdown issue reports. There are no seat fees and no licence cap on URLs; it is built for crawls of tens of thousands of pages, not millions. It can also fail a build on SEO errors (--fail-on error), and it ships an MCP server so AI agents like Claude or Cursor can run and read audits (read-only by default). It is source-available under the Elastic License 2.0, not OSI open source, and we built and maintain it ourselves. It does not do per-client projects, PDF reports, white-label output, keyword research or rank tracking.
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FounderPlaybooks.
What other founders did to grow.
2722 dispatches from hundreds of founders, pulled from the week's best podcasts.
if you just run one campaign with one adset in it all that data will be used to basically fine tune targeting for you so you always want to find the right balance of where do I need granularity to guide the algorithm
Start with a maximally consolidated Meta account — one campaign, one ad set, all signal in one place
Meta's algorithm needs enough signal data to optimize effectively — scattering budget across 10 campaigns and 50 ad sets starves each one. Burke's default for new accounts is maximum consolidation: one campaign, one broad ad set, all signal funneled to the same place. Add campaign branches only as spend scales and after confirming what you're splitting on.
When we do the math at the end of the year and we look at everything we tried — all the biggest fails versus all the biggest wins — what we see is that wins always compensate for fails. I don't like to brag but we did improve our LTV/CAC — we more than doubled it in two years while scaling up on volumes at the same time.
Test Culture: Wins Always Compensate for Fails — Deezer Doubled LTV/CAC While Scaling
Deezer's performance marketing team doubled their LTV/CAC ratio while simultaneously growing volume — primarily by running more tests. The key mental model: evaluate tests in aggregate across the full year, not in isolation. An individual failed test that spent $30K is not a $30K loss; it's a learning that informs a future test that may return $300K in efficiency gains. Fear of failure at the test level kills the portfolio.
There's a play for whatever you're stuck on.
Read all 2722 playbooks