A Different Take on MCP: Introducing MCP Scripting

A lot of the buzz around MCP — Model Context Protocol — has centered on chatbots. You type in a request, the chatbot figures out what tools it needs, and it fires off some API calls behind the scenes. It’s a cool idea and a big step toward making AI more useful.

But at Gumloop, we wanted to try a different approach. Instead of having the AI use MCP tools on the fly, we asked: what if it could build a script that uses them? Something you can run again, edit, and trust to behave the same way each time?

That’s what we’re calling MCP scripting — and it unlocks a whole new way to build with AI.

How MCP Scripting Works (and Why It’s Different)

In traditional MCP implementation, an AI client (usually a chatbot) receives a user prompt, determines which MCP tools to use, fills in the parameters, and makes a live call to each tool — all in one go. It’s dynamic and flexible, but it’s also ephemeral. The moment the task is done, the logic behind it is gone.

MCP scripting takes a more structured route. When you describe a task in Gumloop — say, “get all unread Slack messages from the last 3 days in #general, sent by Rahul with no reactions” — the AI doesn’t just do the task. It writes a script that knows how to do it. That script becomes part of your workflow: editable, repeatable, and scalable. Your propmt becomes a node that can be reused in all of your worfklows.

You’re not prompting a general-purpose AI to guess what you meant — you’re prompting it to build something for you.

When You Want Precision, Not Conversation

Both approaches have value. Chatbot-based MCP is great when you want flexibility in a conversational setting — quick answers, exploratory tool use, lightweight interactions.

MCP scripting shines when you need:

• Repeatability – You want to run the same task every day, without surprises.
• Scale – You’re pulling thousands of rows or chaining multiple tools together.
• Complex logic – You’re filtering, parsing, transforming — and want fine-grained control.
• Easy iteration – You want to tweak part of the logic without starting from scratch.
  

Instead of wondering if your prompt will work, you’re working with a deterministic script that does exactly what you asked — and that you can keep refining.

A More Purpose-Built Client

Another key difference: the MCP scripting interface in Gumloop isn’t a chatbot that happens to support MCP. It’s a purpose-built client designed only to generate scripts that interact with integrations. That means fewer assumptions, less guesswork, and tighter control over what actually gets executed.

The result is a workflow that behaves more like a custom internal tool — one that you didn’t have to code from scratch.

A New Way to Build with AI

MCP scripting isn’t about replacing chat-based workflows — it’s about giving you another, more structured option when you need reliability, control, and repeatability. Where chatbots aim to react in real time, scripting is about building something that lasts. Something you can run, edit, and scale.

If you’re creating workflows that touch real data, involve multiple tools, or need to be reused and refined over time, scripting gives you the foundation to do it right — without needing to write the code yourself.

This is AI not just as an assistant, but as a builder.

And the result? Workflows that don’t just work once — they work every time.