llms discovery headers on every page
Add `Link` and `X-Llms-Txt` headers to normal page responses so an agent can find your machine-readable corpus before it starts crawling blindly.
Why this can grow a startup
A lot of discovery friction happens before an agent reads a single paragraph. When ordinary page responses advertise where the structured corpus lives, the site becomes easier to interpret with fewer wasted fetches. It is a small technical move that makes the content layer easier to find without changing your visible UX.
Source-backed example
Mintlify adds `Link` headers pointing to `/llms.txt` and `/llms-full.txt`, plus an `X-Llms-Txt` header, on every page response so AI tools can discover the files without prior knowledge.
Source-reported evidence
Source: Mintlify Docs · Browse Mintlify Docs tactics
Every page response can advertise `/llms.txt` and `/llms-full.txt` via HTTP headers
Attributed to the cited source; GrowthDex has not independently verified it. Use this as context, not as a forecast, benchmark, or recommended target.
Evidence scope: claims and metrics are attributed to the linked source; GrowthDex has not independently replicated the reported result. Check the original context and validate fit before applying it. Read the evidence methodology.
Source discovered: May 27, 2026
When to use it
Use this when SEO, AI Search, Website is relevant to ai discovery, crawlability, headers and you can run a bounded test with a free budget.
When not to use it
Do not use it as a substitute for customer evidence, a clear owner, or a measurable stop condition. Local platform rules and market behavior still need checking.
How to do it
- Choose one target segment and define the exact next action this tactic should prompt.
- Adapt the tactic to SEO and AI Search, using evidence from Mintlify Docs as context—not as a promised result.
- Record a baseline for: Qualified search visitors who complete the intended next action. Set a guardrail that would make you pause the test.
- Run a narrow pilot and choose a test window that fits the normal decision or use cycle; do not scale before you have observed the full cycle.
- Compare the signal with its baseline. Scale only if the intended action improves without crossing the guardrail; otherwise revise one variable or stop and record what you learned.
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