Semrush competitor analysis in Claude with Linkeddit

Semrush's guide pairs a live data source with an AI that can query it: the Semrush MCP and Claude Code. You tell Claude your goal, give it your site and a short list of competitor URLs, then ask it to pull organic keywords, domain metrics and backlink data for each one. Because the data comes through MCP, Semrush notes you do not export CSVs or switch tabs.

If you do not have a competitor list, the guide goes the other way: give Claude the keywords you want to rank for and let the Semrush MCP pull the domains already ranking for them, which gives you your search competitors rather than the companies you think of as rivals.

Rankings answer it better than a guess. On the test project, across 59 checked keywords, reddit.com had a Google top 10 page on 44 of them at an average position of 2.34 and ranked above plausible.io on 41. posthog.com had 12 and matomo.org had 11. A forum domain and a software rival compete in different ways, so ask Claude to keep them as separate lists.

The footprint also shows AI answers. matomo.org was cited by Google AI Overviews on 11 of the checked keywords and, in the project's latest finished answer run, by ChatGPT 21 times. Semrush data can size a rival's traffic and backlinks, and the Linkeddit footprint shows where that rival is being recommended.

Semrush's guide makes a point worth keeping: do not just ask Claude for keyword gaps, because Semrush's Keyword Gap tool already does that faster. Use the AI to interpret them. It suggests weighting gaps by intent, since 200 keywords a competitor ranks for matters little if most are low intent, and comparing content formats, since a comparison page beating your blog post says more than the gap itself.

Linkeddit adds the topic view. search_research_get groups keywords whose Google results share at least 3 of the same top 10 pages and labels each topic. A gap means you have no page in the top 20 and a competitor page is in the top 10: betterlytics is one in the test run. A cannibalised topic means two of your own URLs compete: simple analytics alternative, best rank 3.

The point holds for Semrush data too. "Real competitive intelligence is not a list of names," said a Claude user on Reddit. Claude should turn the domain and keyword lists into a view of who wins which questions, not hand the list back.

Linkeddit's numbers come from the keywords you track and the checks already run, so here the footprint covers 59 keywords and 47 AI answer samples, not the whole market. Semrush calls use API units, and its plans list 50,000 included units, so a broad pull for five rivals is not free.

Neither connector writes advice. Claude ranks the topics by how weak the competition looks and shows the evidence, and you judge which gaps fit your business. Rerun it monthly and compare: a rival gaining top 10 keywords, or a topic moving from gap to striking_distance after you publish, is the signal worth reading.

Semrush's guide asks Claude which pages are doing the heavy lifting for each competitor, because a few pages usually carry most of a rival's organic traffic. Those are the pages worth studying, and the Semrush organic research tool is where that traffic estimate comes from.

Linkeddit's content_map view of search_competitors_get answers a different question: how a rival builds its site. It reads the competitor's public sitemap and groups pages into editorial sections, such as articles and guides, and templated sections, many pages built from one pattern. Ask Claude to compare both before copying a rival's format.

Pair the two views before acting. If a rival's winning pages sit in a templated section and your topic is a gap, a single strong editorial page may still beat it. If the rival earns its pages one by one, expect a longer fight. Ask Claude to state which case each shortlisted topic is, with the evidence.