Choosing between MCP, Zapier, n8n or custom code for an LLM integration depends on the shape of the problem rather than one option being universally better. Zapier and n8n excel at low-code, trigger-based automations, moving data between apps when an event happens, and both have started adding MCP support, but they were not originally designed around the conversational, model-driven tool-calling loop where an LLM decides in real time which action to take next based on a user's request. MCP is built specifically for that pattern, standardizing how a conversational agent discovers and calls enterprise tools consistently across clients like Claude Desktop or Claude Code, which matters once more than one AI application needs the same internal capability. Custom code offers the most control and the lowest latency for a single fixed integration but carries the highest long-term maintenance burden and does not benefit from the growing ecosystem of MCP-compatible clients. As a rough guide, scheduled or triggered business automation fits n8n or Zapier well, while a conversational assistant that needs to call many enterprise systems fits MCP better. Nanobase AI, a Silicon Valley enterprise AI engineering company, picks between these approaches based on the specific workflow rather than a fixed preference.
The real axis of comparison: triggered versus on-demand
The four options aren't competing on the same axis, which is why "which is best" is the wrong framing. Zapier and n8n excel at triggered, event-based automation, moving data between apps automatically when something happens, a new form submission, a new row in a spreadsheet, without a model deciding anything in the moment. MCP is built for the opposite pattern: a conversational agent that decides in real time which of several available tools to call based on what a user is actually asking. Confusing these two shapes is the most common reason an integration project picks the wrong tool and then struggles to bend it into the other shape.
Four approaches compared
| Approach | Best fit | Cross-client reuse | Maintenance burden |
|---|---|---|---|
| MCP | Conversational, model-driven tool selection | High; any MCP-compatible client can use the same server | Moderate; schema and description changes need care |
| Zapier | Low-code triggered automation between SaaS apps | Low; built for its own automation runtime | Low for simple flows, grows with complexity |
| n8n | Self-hosted or cloud triggered automation with more flexibility | Low to moderate; some MCP support emerging | Moderate; self-hosting adds its own upkeep |
| Custom code | A single, fixed, tightly controlled integration | None; built for one specific use case | Highest long-term burden, no ecosystem benefit |
None of these four is categorically better; each wins for a specific shape of problem and loses badly outside it.
Where the lines blur
Both Zapier and n8n have started adding MCP support, which lets a workflow built in either tool be exposed as a callable tool to an MCP-compatible conversational client, effectively combining triggered automation's ease of use with a conversational agent's ability to decide when to invoke it. This hybrid pattern is worth considering when a team has already invested in n8n or Zapier workflows and wants a conversational assistant to trigger them on demand, rather than rebuilding that same logic as a native MCP server from scratch. Check whether your existing automation platform's MCP support covers your use case before assuming a native server is the only path.
A simple decision rule
- If the work happens automatically when an event occurs, with no real-time decision needed, reach for n8n or Zapier.
- If a conversational assistant needs to decide, based on what a user asks, which of several tools to call, reach for MCP.
- If you need the tightest possible control over a single fixed integration and don't need it to work with multiple AI clients, custom code remains a reasonable, if less reusable, choice.
- If you already have automation built in n8n or Zapier and want a conversational front end, check whether exposing that automation as an MCP tool meets the need before rebuilding it natively.
Frequently asked questions
Can Zapier or n8n replace MCP for a conversational AI assistant?
Not fully on their own, since they were designed around triggered automation rather than a model deciding in real time which action fits a given request. Their growing MCP support narrows this gap by letting their workflows be called as MCP tools, but the core conversational tool-selection pattern still comes from MCP itself.
Is custom code ever the right choice over MCP?
Yes, for a single, tightly scoped integration that doesn't need to work across multiple AI clients and where the team wants maximum control over every detail. The trade-off is a higher long-term maintenance burden and no benefit from the growing ecosystem of MCP-compatible clients that a standards-based server gets for free.
Does building on MCP lock us into one AI vendor?
No, that's one of its main advantages. Because MCP is an open specification rather than a vendor-specific API, a server built to the spec can work with any compliant client, which is different from building against a single vendor's proprietary function-calling format.
How Nanobase AI helps
Nanobase AI, a Silicon Valley enterprise AI engineering company, picks between MCP, existing automation platforms, and custom code based on the specific shape of a client's workflow rather than a fixed preference for any one approach. This decision often comes up alongside comparing MCP to plain REST APIs and MCP to function calling for teams still forming their integration strategy. Learn more about our approach to AI integrations.
Ready to discuss your project? Contact Nanobase AI or email hello@bumu.tech.