We scanned 106 chip companies to see if AI agents can read them. The average grade is F.

Your buyers now research inside ChatGPT, Claude, and Perplexity. So we measured how legible the semiconductor industry actually is to those agents. It is not good.

The way engineers find parts changed. A growing share of technical research now starts inside an LLM — "find me a 3D Hall sensor that replaces the TLE493D, I²C, automotive-grade" — not in a Google search box. The agent answers from whatever it can read and parse on your site.

So we asked a concrete question: can an AI agent actually read the semiconductor industry's websites? We scanned 106 semiconductor, analog, power, RF, sensor, MCU, memory, passives, connector, EDA, distributor, and test-&-measurement companies across 20 agent-readiness signals — robots.txt, llms.txt, MCP, structured data, content negotiation — and scored each 0–100. Public pages only, the same way an agent sees them. You can re-run any of them live in our free scanner.

The headline: the industry isn't ready

Across 106 companies, the average score is 42/100. 82 of 106 score below 50. Not a single company scored an A. Exactly one cleared a B. For an industry whose entire job is precision and documentation, that's a striking gap between how good the datasheets are and how badly machines can read the sites that host them.

The datasheets are excellent. The websites wrapping them are nearly opaque to an agent.

Most agent-ready (top 8)

Least agent-ready (bottom 8)

See all 106 on the full leaderboard →

What almost everyone gets wrong

Three failures show up again and again, and they're the heaviest-weighted checks:

• No markdown content negotiation. Ask most of these sites for Accept: text/markdown and you still get a wall of HTML. A typical homepage costs an agent 40,000–90,000 tokens to read; the same content as clean markdown is ~1,000–2,500. That's the agent burning ~95% of its context just to parse your navigation and tracking scripts.
• No llms.txt. Almost none publish a /llms.txt — the simple index that tells an agent which pages and which markdown files matter. Without it, the agent has to crawl and guess.
• No MCP server. Zero of the scanned sites expose an MCP endpoint, so an agent can't call search_parts or get_datasheet directly. It's stuck scraping rendered HTML — slow, lossy, and often wrong.

Why a low score actually costs you

An agent can't cite what it can't parse. If your specs are locked inside PDFs and JavaScript-rendered pages, the model either skips you or — worse — paraphrases your part wrong and recommends a competitor it could read. The companies near the top of the list aren't doing anything exotic; they simply ship a sitemap, structured data, and machine-readable content. That's the difference between being quotable and being invisible.

Scan your own site

Paste any domain into the agent-readiness scanner and you'll get the same 0–100 score, the category breakdown, and the specific fixes — in about two seconds, no signup.

Methodology: public pages only, scored against a 20-signal agent-readiness set, June 2026. Sites that block automated requests were excluded. Scores are a snapshot and change as sites change — re-run live anytime.