# MCP vs API for agents: when each one fits

MCP and a plain API both let an agent reach outside data. What the agent handles in each, when to pick which, and a prompt to test both on your job.

## Summary

People ask whether an agent should use MCP or call an API directly. The question mixes two levels. An API is how a service exposes its features. MCP is a standard way to package tools so an agent can find and call them. Many MCP servers call an API inside.

So the real choice is about who does the glue work. With MCP, the server author did it. With a raw API, your agent or your code does it.

## What the agent has to handle

The clearest way to compare them is to list the jobs that land on the agent in each case.

| Job | Calling a raw API | Using an MCP server |
|---|---|---|
| Finding what exists | Read docs, or you paste them in | The server lists its tools and inputs |
| Building the request | Agent writes the HTTP call, headers and body | Agent fills named arguments |
| Authentication | You store a key and wire it into the code | Set once in the connection, often a browser sign-in |
| Reading the response | Agent parses whatever shape the API returns | Server may return trimmed, agent-friendly output |
| Errors and retries | Agent or your code handles rate limits and failures | Depends on the server, often partly handled |
| Context size | Raw responses can be long | Depends on how the server shapes output |
| Control | Full, you choose every field | Limited to the tools the server exposes |

None of these rows is a verdict. Each is a trade between effort and control.

## When a raw API fits

Use the API directly when you need a field the MCP tools do not expose, when the job is a fixed script that runs the same way every time, or when you want exact control over retries and storage. A nightly script that pulls one report does not need an agent to discover anything. The code already knows what to call.

It also fits when you are building a product. You will want typed code, tests and logs you own.

## When MCP fits

Use MCP when the agent chooses the work as it goes. You ask for something loose, such as finding the right contact at a company, and the agent has to pick a tool, read the result and decide what to do next. A tool list with plain descriptions is what makes that possible. Discovery is the point.

It also fits when you want to try a data source in ten minutes. Adding a server is quicker than reading docs and writing a client.

## The costs people forget

- Every connected server adds tool descriptions to the agent's context. Many servers at once can crowd it.
- A server author decides what a tool returns. If it drops a field you need, you cannot get it back.
- Several MCP servers mean several accounts, keys and bills, unless one connection covers them.
- A raw API means you maintain the client when the provider changes something.

That third point is the one looot targets. One connection, one balance, many providers behind it. The agent searches the catalog, reads the endpoint's inputs and calls it. If you need a raw call, the same catalog is reachable over HTTP, so you can start with the agent and move a stable job into code later.

## An honest recommendation

Start with MCP while you are exploring. You learn which calls you need fast. When a job becomes routine and you can name every input, move it to a script that calls the API. Keep MCP for the open-ended parts.

Tip: You do not have to pick one for the whole project. Mixed setups are normal: the agent explores through MCP, and a stable job runs as code.

## Test it on your own job

Reading a table only goes so far. Give your agent one real job and have it report what the work felt like.

The answer is specific to your stack, which is the point. For background on the protocol, read [what is an MCP server](https://looot.ai/blog/what-is-an-mcp-server). To see the tools behind one provider, open the [Exa page](https://looot.ai/mcp/exa).

[See the MCP pages](https://looot.ai/mcp)

## Questions

### What is the difference between MCP and an API?

An API is how a service exposes features to code. MCP is a standard that packages tools so an AI agent can discover and call them. Many MCP servers call an API inside.

### Is MCP vs API a real either-or choice?

Not usually. Teams explore with MCP and move stable jobs to direct API calls. The two work together.

### Is MCP faster than an API?

Not by itself. MCP saves setup time because the agent discovers the tools. Speed per call depends on the provider behind the server.

### When should I call an API directly instead of using MCP?

When you need a field the tools do not expose, when the job is a fixed script, or when you need exact control over retries, logs and storage.

### Does MCP use more of the agent's context?

It can. Each connected server adds its tool descriptions to the context, so many servers at once take space.

### Can looot be used as MCP and as an API?

Yes. Your agent connects over MCP, and the same catalog can be reached over HTTP when you want a job to run as code.

## For agents

### Prompt to compare both paths

```text
I want to find the CEO of https://example.com and their work email. First, use the MCP tools you have connected and tell me which tools you used, how many calls it took, and anything that failed. Then describe what you would have needed to do the same job by calling the provider's API directly: keys, request format, parsing, retries. End with a one-paragraph recommendation for this job.
```