When to Build an API vs an MCP

If you're building AI-powered systems, you'll eventually face a decision: should you expose a capability as a traditional API, or wrap it as an MCP tool?

I've been working with both approaches lately, and the answer isn't always obvious. Both work, but choosing the wrong one can make your life harder depending on your goals. It's also not an either-or decision—having both is often ideal. But given resource constraints, you usually need to prioritize one first.

In this post, I'll walk through when APIs make sense, when MCPs are the better choice, and how to think about the tradeoffs across agent usability, observability, developer experience, and interoperability. If you're new to MCP, check out my post on building a secure MCP server in TypeScript for a hands-on introduction.

Understanding the Difference

Before diving into the comparison, it helps to understand what each approach is designed for.

APIs are system-to-system contracts. They define deterministic inputs and outputs with explicit versioning. When you call an API endpoint, you know exactly what you're going to get back.

MCPs are agent-facing interfaces. They exist to shape how an AI model interacts with your system, not just to return data. An MCP tool includes semantic context that helps the agent understand when and how to use it.

The way I think about it: an API answers "what can this system do?" while an MCP answers "how should an agent use this system?" That distinction ends up mattering more than you might expect.

Ease of Use for Agents

APIs Require More Setup Work

Agents can call APIs, but you need to do a lot of upfront work to make it happen smoothly:

• You have to describe when the agent should call each endpoint
• You have to explain how to combine parameters correctly
• You have to define what a successful response looks like

Every API call also costs tokens, attention, and reasoning budget. Agents don't naturally discover APIs—they need to be explicitly instructed on how to use them.

APIs work best when the operation is narrow and mechanical, the parameters are stable and well-defined, and the agent already has a clear plan. Think CRUD operations, simple lookups, file operations, or billing state checks.

MCPs Are Built for Agent Ergonomics

MCPs are opinionated by design, which turns out to be a feature rather than a bug:

• Tool schemas are simpler and more focused
• Semantic context lives alongside the execution logic
• The agent doesn't need to infer intent from raw endpoint definitions

When you build an MCP tool, you don't just exposed functionality. You teach the agent how to think with it via the schemas and context. This makes a significant difference when the agent is exploring options, working through multi-step tasks, or operating in situations where you want to reduce the chance of the model hallucinating non-existent API calls.

In my experience, MCPs reduce agent confusion dramatically in complex workflows.

Observability and Tracking

APIs Fit Existing Infrastructure

APIs integrate naturally into existing observability stacks. You can use your standard logging, metrics, traces, and error tracking without any special configuration.

The challenge comes when an agent calls your API and you want to understand why. Was this call exploratory? Was it part of a larger plan? Did the model misunderstand the API contract? You can add metadata to track this, but now you're building agent-aware telemetry from scratch.

MCPs Have Built-in Semantic Context

MCP calls are already structured in a way that maps to agent behavior:

• Each tool has clear boundaries
• Invocation intent is explicit
• Inputs and outputs are structured

This makes integration with tools like Langfuse much cleaner. Each tool call becomes a single semantic event that's easy to replay and debug. MCP calls also appear within agent traces, making it easy to reason about how the agent intended to use the MCP. If you're building evaluation harnesses or tracking prompt regressions, having this structure built in from the start saves a lot of work.

If you care about understanding tool misuse, tracking prompt regressions, or monitoring agent behavior over time, MCPs give you better observability with less custom infrastructure.

Developer Experience

APIs Are Familiar Territory

APIs win on familiarity. Most teams already know how to build, test, and deploy them. The tooling is mature, and onboarding new engineers is straightforward even if they don't have AI experience.

APIs are composable, testable, mockable, and well-understood. If your primary users are frontend engineers, mobile clients, or third-party integrators, an API is probably still the right choice.

MCPs Enable Faster Iteration

MCPs shine when you're iterating on agent behavior frequently. The feedback loop is tighter:

1. Change the tool schema
2. Run the agent
3. Inspect the traces
4. Adjust the semantics

If your prompt and tool shape are evolving together, and you want to ship behavior changes rather than just new endpoints, MCPs make that iteration faster.

The tradeoff is that MCPs are newer and less standardized. They currently work best for teams that are already comfortable with AI tooling. If you're optimizing for organizational scale and team familiarity, APIs are the safer choice. If you're optimizing for AI, thinking speed, and rapid experimentation, MCPs have the edge.

Interoperability

APIs Work Everywhere

APIs are the universal adapter. They work with any language, any platform, and any client. They survive refactors, organizational changes, and technology migrations.

If you need public access, partner integrations, or long-lived contracts that external systems depend on, APIs are essential.

MCPs Work Best Within AI Systems

MCPs interoperate well with other agents, tool routers, and agent frameworks. They're excellent inside an AI system, but they can be brittle at the edges when you need to integrate with non-AI systems.

The key insight here is that MCPs don't replace APIs; they wrap them. The strongest setups I've seen expose APIs as the stable core that other systems can rely on, with MCPs as the agent-facing interface on top. This gives you the best of both worlds: stability for external integrations and ergonomics for AI agents.

Versioning

APIs Have Mature Versioning Patterns

API versioning is a solved problem. You can use URL versioning (/v1/users), header versioning, or query parameters. There are established patterns for deprecation, sunset headers, and migration paths. Your team probably already has conventions for this.

When you make a breaking change to an API, you can run multiple versions in parallel, give consumers time to migrate, and eventually retire the old version. This predictability is one of the reasons APIs work so well for external integrations and long-lived contracts.

MCP Versioning Is Still Evolving

MCP versioning is less mature. The protocol itself has a version, but there's no standard convention yet for versioning individual tools or servers. If you change a tool's schema, you're essentially changing the contract with every agent that uses it.

In practice, this means MCP changes need to be more carefully coordinated. Since agents discover tools dynamically, a schema change can immediately affect behavior across all connected clients. There's no built-in mechanism for running "v1" and "v2" of a tool side by side the way you would with API endpoints.

For now, I'd recommend treating MCP tool schemas as more fluid during development, but locking them down once agents depend on them in production. If you need to make breaking changes, consider creating a new tool with a different name rather than modifying the existing one. This gives agents a migration path similar to what you'd do with API versioning.

Making the Decision

Here's how I think about choosing between the two:

Build an API when:

• Humans or external systems are the primary users
• Stability and backward compatibility are important
• The operation is deterministic and mechanical

Build an MCP when:

• AI agents are the primary consumers
• You care about shaping behavior, not just returning data
• Agent observability and evaluation matter from the start

Build both when:

• You want long-term flexibility
• You expect agent behavior to evolve significantly
• You don't want to paint yourself into a corner

Final Thoughts

APIs answer the question "what can this system do?" while MCPs answer "how should an agent use this system?"

If you're building AI products and only exposing APIs, you're making your agents work harder than they need to. If you're only building MCPs, you're limiting your integration options.

For most AI applications, the practical approach is to build APIs as your stable foundation and wrap them with MCPs for agent consumption. You get durability from the APIs and better agent ergonomics from the MCPs.