Post: Why I Built Our Make MCP Server Before Make Did — And Why You Should Care About That
I am going to tell you something that sounds either bold or foolish depending on your risk tolerance: we built a custom Make MCP server before Make released one officially. We knew full well that Make would eventually ship their own. We built it anyway. And that decision is one of the better calls we have made in the last two years.
This post is about why we did it, what we learned from running it in production, and why the same logic applies to any automation team that wants to be ahead of the curve instead of on it. If you want the full technical breakdown of what we learned running it day-to-day, start with our field report on Make skills and Claude in production environments — that’s the anchor for everything I’m referencing here.
The short version: waiting for vendor tooling means you start the learning curve the same day everyone else does. Building ahead means you’re already past it when the announcement lands.
What Is the Actual Bet Here?
When we decided to build our own Make MCP server, we were making a specific bet. Not that Make would never build one. The opposite — we assumed they would, and probably soon. The bet was that the gap between now and then had real value. Production experience compounds. Six months of running something in live client scenarios teaches you things that no documentation, demo, or release announcement ever will.
We were right. Make’s tooling has continued to evolve. And when those updates land, we are not starting from scratch. We are comparing notes. That is a fundamentally different position to be in.
The other part of the bet: we accepted that our custom build would carry technical debt. Early-built tooling always does. We knew we would have to refactor parts of it. We built that expectation in from day one. The debt was the price of the education, and the education was worth it.
What Did We Learn That We Couldn’t Have Learned Any Other Way?
Quite a bit. Here are the findings that would not have surfaced without production volume.
Seeding matters more than prompting. We had to seed our MCP with existing Make scenarios so it understood the JSON structure natively. Once seeded, it pulls in credentialing and structural details automatically. You cannot shortcut this step — and you would not know it existed unless you ran the thing long enough to see what happens without it. We cover this in detail in our case study on seeding MCP with existing scenarios.
Complete scenarios without broken modules. This was the biggest surprise. The MCP consistently builds entire scenarios without broken or placeholder modules — something that AI-assisted builders struggle with badly in general. It is particularly strong on HTTP posts: it formats the JSON script and module internals correctly the first time, not as a draft you then have to repair.
Description precision is everything. The system builds exactly what you describe. That sounds obvious until you realize how vague most people are when describing what they want built. Vague input produces vague scenarios. Specific, structured input produces production-ready scenarios. Learning to write tight descriptions is a skill — and we only developed it through repetition.
It asks instead of guessing. When context is missing, the MCP pushes back with clarifying questions rather than silently inferring. That behavior matters in production. Silent wrong assumptions cost time. Clarifying questions save it.
API docs become HTTP modules. Hand it the API documentation for a tool with no native Make module and it formulates the API calls correctly inside generic HTTP modules. This one detail collapsed a recurring problem: the vendor-doesn’t-have-a-native-integration wall that used to mean either waiting or building from scratch manually.
Routed error handling, not generic catch-alls. Tell it what to do when a specific error fires and it builds routed handlers — not generic try/catch logic, but specific routing per error condition. That is a meaningful difference in production stability.
Self-diagnosing error handler. We extended the MCP to act as an error handler for our own scenarios. When a scenario errors, the MCP reads it, identifies what is broken, and sends our technicians an email with analysis and a suggested fix. Research time per error dropped from 20 to 30 minutes down to a glance at an email. The human is still in the loop — but the research work is gone.
None of these insights came from reading about MCP architecture. They came from running it. That is the point.
Is There a Real Counterargument Here?
Yes, and I want to be fair about it.
Building custom tooling ahead of vendor releases carries real costs. Engineering time. Maintenance burden. The certainty that some of what you build will be superseded. If you have a lean team and every hour is allocated, this is not a trivial trade-off.
There is also a version of this that goes wrong: teams that build custom tooling and then refuse to migrate when the vendor ships something better, because they are attached to what they built. That is a different problem — ego getting in the way of good decisions. We treated our custom MCP as a learning vehicle, not a permanent solution. The moment a better option exists and we have validated it, we migrate. No attachment.
And honestly — not every team should do this. If your automation practice is early-stage, if you are still learning the fundamentals of Make scenario architecture, building a custom MCP server is not the right next step. Get the foundation solid first. The advanced tooling pays off when you have enough production volume to actually use what it teaches you.
What This Means for How You Think About Vendor Timelines
- Every major automation platform is adding AI capabilities. The question is not whether they will ship — it is whether you will be learning when they do, or already past the learning curve.
- Production experience is not transferable from someone else’s case study. You have to run the thing to earn the insight.
- Technical debt from early builds is real but manageable. Treat it as tuition. Budget for the refactor when the vendor ships the better version.
- The teams that will be positioned as credible AI-automation partners 18 months from now are the ones running things in production today — not the ones waiting for the polished release.
- Vendors ship to the median user. Custom builds serve your specific workflow. Both have value. Know which one you need and when.
What Should You Do Differently Starting Now?
If you take one thing from this post, make it this: pick one area where you are currently waiting on vendor tooling and ask yourself what the cost of that wait actually is. Not the cost of building early — the cost of waiting. Learning curve lag is invisible until you are behind it.
Here is a practical path forward:
Step one — validate the gap. Identify what the vendor has not shipped yet that you actually need. Not every gap is worth filling with a custom build. Some are. The filter is: does this gap create recurring friction in production right now?
Step two — scope narrow. We did not build a full replacement for anything. We built one specific piece — a Make-connected MCP — and ran it in production. Narrow scope means faster feedback and manageable debt.
Step three — seed it with real data. This applies directly to MCP builds but is a general principle: custom tooling trained on your actual scenarios and workflows outperforms generic tooling trained on nothing. Put the setup work in.
Step four — document what you learn as you learn it. The value is not just in having the tool. It is in the documented production lessons that compound into a real competitive advantage. We turned ours into the five production lessons from running a custom Make MCP server. That documentation is now part of how we train, how we sell, and how we differentiate.
Step five — plan the migration. Know in advance what would cause you to migrate to the vendor’s native solution when it ships. Set the criteria now, before you are attached to what you built. This keeps the decision clean when the time comes.
If you want to understand how we apply this thinking across the full automation stack — not just MCP architecture — the six signs a Make partner has real AI production experience breaks down what separates teams with genuine depth from teams that are still reading about it.
The Bottom Line
We are a Make Gold Partner. We take that seriously — it means we are running Make at a level that warrants that credential. Part of what that means is not waiting for Make to tell us what is possible. It means finding out ahead of them when we can, feeding that back into our practice, and being ready to integrate their improvements from a position of experience rather than curiosity.
We built before the vendor shipped. The tooling has debt. The lessons are worth more than the debt. And we are already past the learning curve that the rest of the industry is just starting now.
That is the bet. And it paid off.
Keep Automating,
Jeff Arnold
Founder & CEO, 4Spot Consulting — Make Gold Partner
Information in this article is deemed to be accurate at time of publishing. 4Spot Consulting reviews and updates content periodically as best practices evolve.
