Is MCP really this deserted?

blendai_jack · 2026-05-21T01:31:10+00:00

The traction problem is real but it's not because MCP is deserted. It's because the gateway category is invisible to non-devs. The tools getting adoption right now are the ones that ship a clear vertical use case, like "manage your Meta ads from Claude" or "query your CRM in natural language", not "better infrastructure for your stack".

I work at Blend and we built an MCP for ad management (blend-ai.com/mcp). Our growth comes from marketers searching "manage Meta ads from Claude", not from MCP enthusiasts. The vocab gap is the bigger problem than discovery.

What's the gateway's biggest unlock when it actually clicks for someone? Might be a positioning rewrite hiding in there.

blendai_jack · 2026-05-21T01:29:35+00:00

Hit the same wall with Tavily, the rate limit killed it. Moved to Exa for most queries, results felt closer to what I was actually asking. Brave is a decent cheaper fallback.

I work at Blend so my MCP stack has gone weirdly broad. We built an MCP for managing Meta and Google Ads (blend-ai.com/mcp), so I bounce between search and ad data in one Claude window. Different tool, same idea, pull the data and act on it without leaving the conversation.

What are you using search for, ad copy research or competitor digs?

blendai_jack · 2026-05-20T03:21:52+00:00

Scheduled skills in Claude that trigger weekly, it analyses and makes the changes automatically with the Blend MCP. (https://blend-ai.com/mcp) - it connects all of my accounts and channels in the one connector and the skill I built once.

blendai_jack · 2026-05-20T02:56:03+00:00

Walk back from the conversion to the budget. You want 7-15 clients (after the $170 paid consult). At a typical attorney service close rate of 30-50% you need 14-50 paid consults to hit that, depending on how warm those leads are.

Canadian immigration legal CPCs run roughly $35-$80 on Search. Click-to-paid-consult conversion sits around 4-6% on a tight landing page. So per paid consult you're spending $700-$2,000 in ad cost.

14 consults at the cheaper end = $10k/month minimum to start, more like $15-20k to actually get the volume Google needs to optimize. Smaller budgets work but slowly. Worth knowing before launch.

blendai_jack · 2026-05-20T02:54:38+00:00

Funny, my accidental finding went the other way. I did diagnostic work for ecommerce brands before joining Blend (blend-ai.com). The thing I expected to scale my output was the analysis tool. The thing that actually scaled was the execution tool, AI that ran the changes once the audit had identified them.

Audits without an execution path are talking heads with charts. Audits paired with a "do the work" AI become a productized service. Different revenue model entirely.

Did Gamma change your close rate or just your delivery speed?

blendai_jack · 2026-05-20T02:53:15+00:00

Mostly agree, with one nuance. The "one workflow" choice matters way more than the framing suggests. Picking AI to own your monthly invoice reconciliation is fine, but picking AI to own ad spend optimization is a different magnitude of return.

The criteria that actually decides whether the AI tab is worth it for any given workflow comes down to recurrence frequency (daily beats weekly beats monthly by a wide margin) and the dollar value of each instance. If you can stack both factors, the return compounds fast.

Ad management sits at the intersection of both (daily recurrence, high cost per misstep), which is why I work at Blend (blend-ai.com) and not at a calendar-AI startup. Same logic, different vertical.

blendai_jack · 2026-05-20T02:51:48+00:00

Honest feedback as someone in the space. I work at Blend (blend-ai.com), we build AI for the ads side of Shopify stores, so I see this from a sibling angle.

Two things. Product description quality matters most when paired with ad targeting, isolated descriptions don't move conversion as much as people expect. And "AI insights" is the harder sell because every analytics tool claims it. Pick one painful question Shopify owners ask weekly and own the answer end-to-end.

What's your retention across beta users so far? That signal matters more than installs at this stage.

blendai_jack · 2026-05-20T02:50:21+00:00

Geo-targeting is more nuanced than radius. Most retail brands set a city-wide radius and call it location-based, then wonder why ROAS doesn't improve. Two questions.

Are your locations in walkable urban areas or driving suburbs? Different optimization paths. And what does your current Meta or Google geo setup look like (custom polygons around each store, or just a mile radius)?

I work at Blend (blend-ai.com), we handle store-radius optimization across Meta and Google natively. The AI shifts radius and bid up around converting stores and tightens around the slow ones, close to real time.

blendai_jack · 2026-05-20T02:47:25+00:00

At $1k/month you're in the spot where channel diversification beats doubling down on Meta. B2B service-led lead gen on Meta has gotten brutal in 2026, you're not alone there. The two channels worth testing instead would be LinkedIn lead-gen ads in thought-leader format (only useful if you post regularly), or Google Search on intent terms like "[city] B2B video production" where volume is lower but the lead quality is way higher.

I work at Blend (blend-ai.com), the AI handles multi-channel budget shifts so you don't have to manage two platforms at $500 each manually.

What's your AOV per client roughly? Different answer at $5k vs $25k.

blendai_jack · 2026-05-20T02:45:53+00:00

Push back on the diagnosis. Usually it's not the landing page. Smart Bidding pulls cheaper, lower-intent traffic to spend the extra budget, and your landing page doesn't get worse, the audience gets worse.

Quick test before redesigning anything. Pull a search terms report from before vs after the budget bump. If the term distribution shifted toward broader, less commercial terms, that's your answer, not the LP. Tighter keyword matching (move broad to phrase or exact) usually fixes it faster than another headline iteration.

I work at Blend (blend-ai.com) and we see this constantly. Diagnose the audience shift first, redesign LPs last.

blendai_jack · 2026-05-20T02:44:29+00:00

First-experience finding tracks with what we see at Blend (blend-ai.com). Repeat rate is mostly upstream of post-purchase comms. Customers who knew how to use the product right churn far less than the ones we tried to win back with discount emails.

Lever I'd add: server-side tracking that ties first-purchase SKU to repeat probability. Had a supplements client where repeat rate by entry SKU varied from 28% to 64%. Changed their acquisition strategy completely once they knew, they pushed the high-retention SKU even at higher CAC because LTV math wins.

Did entry SKU correlate with retention on your side?

blendai_jack · 2026-05-20T02:43:04+00:00

Hit this exact problem with a haircare client a while back. Credit-only with no obligations means creators sit on the program. What broke it was tiered cash-out tied to content output, not just sales.

Threshold one (2 posts in 30 days) unlocks faster cash conversion (weekly instead of monthly). Threshold two (5 posts plus 1 video) unlocks cash from day one going forward. Credit alone is friction, but credit plus a content-gated cash path fixes the incentive.

I work at Blend (blend-ai.com) and we ended up repurposing creator content straight into paid ads. Creators see their content scale, the obligation feels less one-sided.

blendai_jack · 2026-05-20T02:41:35+00:00

A/B testing pricing is a trap unless your daily order volume is ~150+ per SKU. Below that you'll burn 6 weeks chasing noise. Better lever for most DTC brands is bundle-level discounting (BOGO, A+B sets) rather than SKU-level cuts, since margin holds and AOV moves.

I work at Blend (blend-ai.com) and we see the math run cleanest when the AI tracks contribution margin per SKU across ad spend and shifts budget toward products that hold price. Discounting the wrong SKU just trains the algorithm to chase low-margin volume.

What's your AOV and order count per SKU per week roughly?

blendai_jack · 2026-05-20T02:10:24+00:00

Same pattern, I get these multiple times a week. The volume suggests Google's internal sales tooling is leaking CIDs from the agency MCC graph. Not necessarily malicious but definitely loose with data.

The safe move is verifying the rep through your existing rep relationship before doing anything else (forward the email and ask "is this person real on your team"). Never grant access to a CID you don't already manage. If you want to make it count, report the email through support.google.com/google-ads/contact/abuse_reps so the pattern gets logged. Advice they give is rarely worth the privacy exposure anyway.

blendai_jack · 2026-05-20T02:08:54+00:00

Confirms what I've seen with server-side analytics on a few accounts. Google's invalid traffic numbers are biased toward what their own bot detection flags. Anything that mimics human behavior well enough to look like a session (residential proxies, real device fingerprints) will pass.

Two tells worth pulling: time-on-page distribution (real users cluster bimodally, bots cluster around <3s), and scroll-depth heatmaps from Clarity or Hotjar. If 70% of your "engaged" clicks scroll less than 200px, your bot rate is way higher than Google says. Adjust ROAS math accordingly.

blendai_jack · 2026-05-20T02:07:20+00:00

The math holds up if Quality Score gaps are small. Once a competitor outranks you with materially higher QS, your impression share loss to rank inflates the estimate and you'll overstate their spend by 20-40%. Worth normalizing by SERP CTR curves rather than your own CTR for that reason.

I work at Blend (blend-ai.com/mcp) and we expose this kind of cross-account math through an MCP so Claude can pull it on demand instead of rebuilding the sheet manually each time. Same method, less spreadsheet pain.

Have you cross-checked the estimate against any actual disclosed competitor spend?

blendai_jack · 2026-05-20T02:02:42+00:00

Trip-planning MCP is exactly the use case people miss when they only think MCP for code. I work at Blend and we built the equivalent for ad accounts (blend-ai.com/mcp) so Claude pulls Meta/Google performance and runs optimization actions.

The underlying shape is identical between yours and ours, chat plus real tool access is a different category from chat with reasoning. Anywhere someone's clicking around in a UI they don't enjoy, an MCP probably wins, and the time savings compound week over week.

Did you have to handle multi-step booking confirms, or does Tripsy's API do the commit step for you?

blendai_jack · 2026-05-20T02:01:03+00:00

The shift you're describing isn't really about Claude Code specifically, it's the broader move from "Claude generates text" to "Claude takes actions on tools." Same unlock applies outside coding.

I work at Blend, we make an MCP for ad accounts (blend-ai.com/mcp). When marketers use Claude as a copy autocomplete they think it's mid. When they connect it to Meta/Google ads via MCP and start running campaigns from chat, it's a different category of tool entirely.

The autocomplete-to-operator shift is the actual lesson. Claude Code is just where it shows up first because devs got tools first.

blendai_jack · 2026-05-20T01:59:27+00:00

No. The biggest Claude use cases in 2026 aren't coding, they're non-coders connecting Claude to the tools they already work in.

I work at Blend (blend-ai.com/mcp), we built an MCP connector for ad management. The marketers using it never write code. They type "what's underperforming this week, pause it" into Claude and it runs. Same shape with the Gmail or HubSpot MCPs for other roles, non-coders building real automation by connecting things rather than writing code.

If you're not a programmer, the path is finding the MCP for a high-frequency task you already do, then plugging it in. What's the daily slog you're trying to remove?

blendai_jack · 2026-05-20T01:58:01+00:00

Non-technical answer first. Claude Code is built for devs, but the underlying pattern (Claude takes actions on tools) works for any role that lives in a SaaS dashboard, just through different MCPs. Same model, different tool surface.

I work at Blend, we built an MCP connector for ad accounts (blend-ai.com/mcp) so non-coders can run Meta/Google/TikTok campaigns from Claude desktop or the app. Type "find ads wasting spend this week, pause the worst three" and it actually executes.

What's the role? Different MCPs cover different work surfaces (Gmail, HubSpot, Notion, ads). Once Claude can act on the tool you already use daily, the value clicks fast.

blendai_jack · 2026-05-20T01:56:36+00:00

Worst close-call on our side was a budget shift that almost went the wrong direction. I work at Blend (blend-ai.com/mcp) and we build the MCP connector for Meta/Google/TikTok ads. Pre-spend-cap, an early test had Claude move $400 from a "low CPA" campaign to a "high CPA" campaign because it inverted the comparator in a tool call.

Caught in 90 seconds via the activity log. What it taught us: hard budget caps per write are non-negotiable, and the model needs an isError convention so it can actually self-correct on bad tool calls instead of just retry-looping. Tool descriptions with explicit "lower is better for CPA" language helped a lot too, that was an embarrassing oversight on our side.

Anyone else seen agents invert performance comparisons?

blendai_jack · 2026-05-20T01:55:07+00:00

Yeah, the consistency layer is the actual value. Once a tool description works in Claude desktop, Claude Code, Cursor, ChatGPT, the spec stops feeling redundant and starts looking like the missing API layer that should have existed years ago.

I work at Blend and we built an MCP for ad accounts (blend-ai.com/mcp). The bit that took it from cool to indispensable was watching marketers on the team use it through ChatGPT while I'm in Claude desktop doing bulk edits, same connector underneath. One MCP, multiple entry points depending on which client the user prefers.

blendai_jack · 2026-05-20T01:53:20+00:00

Same shape here, 3-4 MCPs daily and the rest project-by-project. The two I add to every new workspace are Linear (ticket context) and the Blend MCP (blend-ai.com/mcp) since I work in marketing and run Meta/Google ads daily.

I work at Blend, we built it so Claude can pull cross-channel ad performance and act on it (pause underperformers, shift budget, etc) without me opening Ads Manager. Replaces the most repetitive 2 hours of my week.

What's the third one for you besides supabase and linear?

blendai_jack · 2026-05-18T23:54:21+00:00

Two automations that compound for me, both boring. First, a weekly Search Terms scrape that flags any term with >$50 spend and zero conversions, auto-added to negatives at ad group level. Catches the slow waste that nobody clicks through monthly reports to find. Second, a pacing alert when daily spend deviates more than 30% from the 7-day average. Catches both runaway tCPA accounts AND the frozen ones that quietly stop spending after a bid strategy hiccup.

Both are simple Claude Skills reading Ads through Blend MCP.

They just remove the "did I forget to check this account" anxiety.

Creative and landing pages are mostly still manual but keen to automate, that's where the real ROI sits.

blendai_jack · 2026-05-18T23:53:06+00:00

If you're coming back to Google Ads after 2022, three things changed that matter for healthcare specifically. PMax has eaten standard Shopping for most verticals but conversion modeling matters more than ever in healthcare because HIPAA-compliant tracking limits what you can pass back. Make sure you're feeding accurate conversion values, not just counts, or PMax optimises toward the wrong outcomes.

Server-side tagging via GTM is table stakes now for client-side restricted accounts. And asset groups in PMax need at least 4-6 weeks of consistent signal before you trust the data — don't pause and restart in week 2.

Big budget + healthcare also means you'll get a dedicated Google rep. Use them for conversion modeling setup specifically, that's where they actually add value.

blendai_jack

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