Comparison
MCP vs REST API — When to Use Which
In Smithery's 756-run benchmark, native MCP hit 92% success versus 76% for a raw REST API with specs. Here's when each is the right call — and when neither is.
The benchmark data
In Smithery’s 756-run benchmark (Smithery, “MCP vs CLI is the wrong fight” (Mar 2026); 3 APIs, 8 experiment families, published Mar 17 2026 by Henry Mao and Rach Pradhan), success rates formed a clear ladder as agents got more structured support:
| Interface | Success rate | Source |
|---|---|---|
| Raw API, blind, no docs | 53% | Smithery |
| Raw API + specs | 76% | Smithery |
| CLI with descriptions + search | 88% | Smithery |
| Native MCP | 92% | Smithery |
On a smaller head-to-head suite, native MCP also used less than half the tokens of a well-designed CLI tree (28,838 vs 82,942 median tokens) and finished in under half the time (10.4s vs 24.9s), in the same Smithery benchmark. On the full 826-tool GitHub catalog, native MCP hit 100% success against 88% for the best CLI configuration, again in Smithery’s reported results.
About this evidence: This is a 756-run benchmark across a specific harness and a small set of APIs. It is a strong signal for tool-calling agents, but results vary with API familiarity and agent design, so treat it as evidence for this context rather than a universal law.
The familiarity effect — this is the part that actually decides the call
The same Smithery benchmark tested blind calling against APIs with different levels of training-data familiarity, and the spread is the real story:
| API | Success rate | Why | Source |
|---|---|---|---|
| GitHub REST (well-known) | 92% | Deep in the model’s training data | Smithery |
| A niche REST API | 42% | Barely represented in training data | Smithery |
| An unfamiliar GraphQL API | 17% | No familiar query shapes to lean on | Smithery |
A raw REST API can perform close to MCP when the API is famous enough that the model already knows it. That advantage disappears for anything the model hasn’t seen — and enterprise internal APIs have zero training priors by definition. That’s exactly where MCP, and good descriptions generally, matter most.
GraphQL specifically struggled on its own — 17% success and 174,532 median tokens, against MCP’s 88% and 33,149 tokens on the same task, in Smithery’s comparison. Query flexibility didn’t translate into agent-friendliness. As the benchmark’s authors put it: a single GraphQL tool that could do anything performed worse than ten specific tools that each did one thing well.
A working rule of thumb
Smithery’s own framing, which lines up with Emmanuel’s Agent Access Stack: if a capability is bespoke to your team, it’s probably a skill. If it should be maintained by the vendor and used by many agents, it belongs in an MCP server. CLI tools serve local, terminal-native workflows — coding agents, CI/CD, DevOps.
When MCP is the right call
- The API is unfamiliar to the model — internal, niche, or new — so there are no training priors to lean on.
- Multiple agents or clients need the same capability, and you want one contract instead of many.
- You want structured, typed tool definitions with descriptions the model can act on directly, rather than a spec it has to interpret.
When REST (with good specs) is enough
- The API is well-known and already has strong training-data coverage, or your consumers are mostly human developers today.
- You’re not yet sure agents will be a meaningful traffic source — building an MCP layer on top of a shaky API just wraps the shakiness.
- You need one team’s small internal capability, not something shared broadly — a well-described API endpoint, or a skill on top of it, may cover it without the overhead of standing up and maintaining a server.
The bottom line
Design a good AI-ready API first. Native MCP consistently outperforms raw API calls in these benchmarks, but the gap narrows sharply for well-known APIs and widens sharply for unfamiliar ones. Build the API right, then decide whether MCP is worth wrapping around it based on who’s calling it and how well the model already knows it. If you need a practical audit pass, use the 12 things every API needs to be agent-ready checklist.
References
- Smithery, “MCP vs CLI is the wrong fight”, published Mar 17 2026 by Henry Mao and Rach Pradhan.
FAQ
- What is MCP, in one sentence?
- Model Context Protocol — a way for AI agents to interact with external tools and services, using structured tool definitions instead of a full API spec the agent has to interpret on its own.
- If MCP performs better in benchmarks, why not build MCP for everything?
- Because the benchmark advantage comes from removing ambiguity for the agent — and a REST API with a well-known shape, backed by strong training data, already has low ambiguity. MCP earns its keep most where an API is unfamiliar to the model, or where the same capability needs to be shared across multiple agents or clients.
- Why do we need to think about AI when designing APIs at all?
- AI agents and LLMs are increasingly the ones consuming APIs, not just human developers. They need clear error messages, consistent patterns, and documentation they'll actually be able to use — and they're also a new security surface, since agents can be manipulated via prompt injection the way a careless human developer can't.