Should you use MCP or build a custom integration to connect AI to your tools?

MCP vs. Custom Integration: How to Connect AI to Your Tools

Use MCP when a tool or data source will be reused across multiple models, apps, or teams — you integrate it once and everything speaks to it. Build a custom integration when you need a tight, high-control connection to a single system and the standard overhead isn't worth it. For most organizations scaling agents, MCP wins because it kills the N-models-times-M-tools integration explosion; custom is the exception for bespoke, performance-critical paths.

Both get an AI model talking to your systems — a database, an API, an internal service. The difference is whether you build that connection once as a reusable standard or wire it bespoke each time. It’s the classic standard-vs-custom trade, applied to tool use.

1. Custom integration: maximum control, no reuse

A custom integration is code you write to connect one model to one tool. You control everything — auth, payload shape, error handling, performance — and for a single tight path that’s a real advantage. The cost shows up at scale: every new model or app needs its own wiring to the same tool, and you maintain all of it. That’s the N×M integration explosion.

2. MCP: integrate once, reuse everywhere

The Model Context Protocol standardizes the connection. A tool exposes itself as an MCP server once, and any MCP-aware client can use it — no per-model glue. Block’s rollout of agents to 12,000 employees, which Angie Jones walked through on the show, leaned on exactly this: MCP turned tool access into something shared across the org instead of re-integrated per team.

3. MCP is an interconnect, not magic

Airbyte’s Michel Tricot put the boundary well on the show: “MCP is not dead — it’s just an interconnect, a way of connecting an LLM to an external system.” A thin MCP wrapper over a weak API is still weak. MCP standardizes how you connect; it doesn’t fix what’s on the other end, and a bespoke integration can still beat it on a performance-critical path.

How to choose

Reused across models/apps/teams, or you want the broader MCP ecosystem → MCP. A single, bespoke, performance- or security-critical path → custom. Many orgs do both: MCP as the default connective tissue, custom integrations for the few places that earn the extra control.

Why it matters

The integration layer is where agent programs stall — not the model. Defaulting to custom everywhere buries teams in glue code; defaulting to MCP turns tools into shared infrastructure, which is why it spread fast once agents went from demo to deployment.