u/berfin-cezim

I spent 15 minutes this Monday morning using Claude with two MCPs to audit our blog and plan the social distribution. Here's what I did and what came out.

I work at Sociality.io and I update articles regularly, but deciding which ones to prioritize is always the stressful part with GSC, Ahrefs, Cloudflare, back and forth, never feels conclusive. So, I connected all of it to Claude at once and let it run.

What I fed it 👉 Ahrefs MCP for live rankings and traffic data (no exports), three weeks of Cloudflare data showing which pages AI crawlers were actually hitting, our blog sitemap, and Sociality MCP to pull LinkedIn post performance for the distribution part.

Ahrefs MCP

Ahrefs MCP

The most AI-crawled pages were our plain analytics guides. LLMs are already citing us for analytics queries constantly, but none of those pages mention our MCP product. That's the whole insight.

Scoring

I shared a rubric, each article rated 1–5 on CF bot activity, SEO opportunity, AI/MCP relevance, business relevance, and effort. Total out of 20. Claude applied it across 23 articles.

The skip list was honestly the most useful part for me. Articles I'd been meaning to update for months got cut in seconds. The "social listening" post I was convinced had strong AI bot traffic? Actually 2 requests/week.

Then I asked it to look at our LinkedIn too.

Connected Sociality MCP to pull our last 3 months of LinkedIn posts and asked Claude what had actually worked.

Sociality MCP

Uncomfortable finding 👉 Text-only posts averaged 249 impressions and 13.9% engagement. Photo roundups averaged 80 impressions and 8.1% engagement.

From there, it mapped each blog update to a post format with suggested hook copy and a 4-week schedule.

Sociality MCP

Sociality MCP

Sociality MCP

Stack if anyone wants to replicate:

Claude (regular claude.ai, no code), Ahrefs MCP, Sociality MCP (free trial available), and the Cloudflare data.

That last one is massively underused. It shows you which pages LLMs are actively pulling as sources, as direct a GEO signal as you can get without being inside the model.

Happy to answer questions if you have any especially about Sociality MCP.

I tested a competitor analysis workflow with Sociality MCP because checking competitors manually always turns into a bigger task than I expect.

As the marketing person at Sociality.io, I usually open each competitor page, look through recent posts, compare formats, check engagement, read comments, note down hooks and CTAs, and then try to turn all of that into something useful for next month. This time I asked AI to do the first research pass with our social data and competitor data already connected.

The prompt was basically this.

Compare us with our tracked competitors for the last 60 days. Show where they are doing better, where we are stronger, what we should test, what we should avoid copying, and give us post ideas for next month.

I did not need to export reports or paste screenshots, which was already a big improvement because that is usually where this kind of work gets slow.

Our stronger posts were mostly simple and specific. The MCP launch post had 8.5% ER, the MCP teaser had 11.3% ER, and one weekly roundup reached 13.9% ER. The weaker posts were mostly the repeated roundup format with low impressions, generic hooks, and not much reason for people to comment.

https://preview.redd.it/0cpj7j2xon6h1.png?width=1534&format=png&auto=webp&s=98317b7fafe3c8a4f20657b9f004e036745ca625

Competitors were doing better with posting frequency, format variety, question-based CTAs, comments, and opinion-led posts. But copying them directly would not make sense for us because our more differentiated angle is the MCP and AI workflow side.

https://preview.redd.it/38dv4w5zon6h1.png?width=1534&format=png&auto=webp&s=622991a5c0dea7c67ea6a5befbde733322c2fefa

The ideas that made sense were to post 3 to 4 times a week, make roundups more about what each update means, test carousels and short videos, ask more direct questions, and use more real AI workflow examples instead of generic social media tips.

https://preview.redd.it/3nyr89kyon6h1.png?width=1534&format=png&auto=webp&s=67cf2539a9df7ad407b9e8372b5b6fd53f1dce11

I would still check the actual posts and comments manually before using the ideas, because numbers do not always explain brand fit or audience quality. But for the first pass, this saved a lot of profile-by-profile checking and made the next steps easier to see.

How do you usually do competitor analysis for social? Do you have a real process, or is it mostly manual checking and gut feeling?

I tested a workflow recently that felt useful enough to share.

I was trying to answer a pretty normal marketing question: Which YouTube creator would be the best fit to mention Sociality MCP? (I work at Sociality.io as a content marketer, and I used Sociality.io's social media MCP as I really needed to analyze those YouTube creators.)

I already had a shortlist of channels. Some covered AI, MCPs, automation, developer tools, social media APIs, and tool reviews. At first glance, several looked relevant, but choosing a creator for a product mention is not just about topic overlap.

I’d go through this manually. Open each channel, check subscriber count, review recent videos, compare views and engagement, understand the creator’s usual angle, and make a decision from scattered notes.

This time, I used Sociality MCP to analyze the shortlisted YouTube channels side by side as potential influencer partners.

Then I asked it to compare the channels, review key YouTube metrics, and help identify which creator looked like the strongest fit for a potential product mention.

The useful part was the follow-up questions:

Which channel has the strongest engagement rate?
Which creator talks about MCPs most directly?
Which audience would understand the product fastest? Which mention would feel natural instead of forced?

Option 1 came out as the strongest fit, even though it was one of the smaller channels in the shortlist, with around 670 subscribers. The reason was relevance. The channel had a strong overlap with social media APIs, tool comparisons, WhatsApp API, scheduling tools, and similar workflows. It also had the highest amount of tool review-style content in the group.

Option 2 was the strongest backup. It had a much larger audience, around 13.9k subscribers, and much higher average views per video, around 10k, compared with Option 1’s roughly 260. It also had strong MCP relevance, with 18 MCP-related videos and 10 AI tool-related videos in the dataset.

The difference was mostly about campaign fit.

Option 2 had stronger reach and clearer MCP credibility. Option 1 had a more natural connection to social media APIs and tool comparison content, which made the product mention feel less forced.

That distinction mattered because more reach does not always mean better fit.

I still would not fully automate creator selection. Before outreach, I’d still manually check recent video quality, comments, audience tone, brand fit, and whether the creator’s style actually matches the campaign.

But as a first research pass, this was much faster than checking every channel one tab at a time.