MCP vs API: What to Choose for AI Agent Development?
You, your mother, and your cat have already heard everything there is to hear about AI – but what about AI agents? Instead of using an LLM as a chatty search engine, an agent is closer to the robotic servant of our dreams. However, somebody has to develop those agents in the first place. And one of the major questions arising today is about the tools your LLM will use. When developing an AI agent, do you give it access to MCP servers or APIs? That’s what the article is about.
What Is an AI Agent?
Your regular LLM is nice and all, but it can’t really do stuff. At the most basic level, an AI can only tell you things based on what it has been taught. For the longest time, it couldn’t even search the internet. And it cannot interact with the world in other ways, like browsing websites, filling out forms, or turning on the smart lights in another room.
An AI agent is a much grander application of the technology. With an LLM at its core, an AI agent can, without close human supervision, carry out complex tasks: writing reports, booking vacations, aiding software development – the works. But to become agentic, one of the things it needs is tools: special software to allow it to interact with its digital environment. Those tools largely come in the shapes of APIs and MCP servers.
What Is an API?
API – application programming interface – is a concept that is in no way new. It’s a framework for allowing software to interact with each other. Today, most people say API and mean web API.
As an app can’t just look at another app’s interface to get data like a human would, the devs have to do the heavy lifting. They’re the ones setting up endpoints for specific tasks. So where a human would click a button on the user interface to achieve a specific result, an app queries the endpoint. With a skilled-enough developer, an AI agent can use an API just like that.
API Pros
- Deterministic: APIs don’t think, APIs don’t reason, they always deliver result Y for input X. It’s great for operations where precision of output trumps other considerations – like in healthcare, government services, and financial operations.
- Grand processing power: API are very efficient at carrying out large-scale data processing tasks without running into any issues that an agent might encounter, for example, by missing data due to pagination.
- Speed: once again, due their unthinking nature, APIs work fast. A query to an endpoint has a list of defined procedures to follow, no need to spend time reasoning what tool would be best.
API Cons
- Manual labor: API endpoints have to be described well for the AI agent to understand what they can do. And since APIs aren’t usually made with AI in mind, you, the agent developer, will have to do the heavy lifting.
- Authorization and security risks: if an agent is hooked up directly to an API, it will handle all the login/authorization credentials. The developer needs to ensure that any OAuth or other security data isn’t misused or leaked by the agent.
- Stateless and contextless: APIs don’t carry memory of whatever was done before, and maintain no context for subsequent tasks. The AI agent would have to provide those things, and that means setting up the agent properly.
- Scaling: so you decided to use a different LLM or build a new agent. Congrats, you have to rewrite all the endpoint definitions for that new model. While all AIs understand natural language, they have been trained to understand queries and output schemas differently based on the developer. That’s just the natural outcome of trying to make LLMs hallucinate less.
What Is an MCP?
MCP – Model Context Protocol – is an Anthropic-designed standard for easy integration of digital tools and AI. To simplify, the magic is in the MCP server, which acts essentially like a translation service. An LLM can ask an MCP server for a resource in natural language, and it will then translate the request for the tools the server was made for, and vice versa.
An MCP server is usually created by the official developers of an application, similar to APIs. In fact, it is a fairly common approach to bundle one’s APIs into an MCP server for LLM use. And just like that, instead of AI developers having to do integration work anytime a model and API have to work together, there’s a single standard that works with them all.
MCP Pros
- Less hardcoding: when an MCP exposes tools and resources, it’s done so with descriptions in natural language, which AI understands. As such, the AI developer doesn’t have to code for all the functions like they would with an API.
- Increased security: as the MCP server manages direct access to APIs, it’s also where the OAuth tokens, API keys, and other sensitive information are handled. The AI doesn’t see them, so it can’t leak or misuse it.
- Stateful resources: one of the three types of primitives an MCP server can expose to an AI is resources – to put it simply, data like log files, database contents, the works. These non-interactive primitives then work as memory, providing the agent with the ability to task the context and progress of the task – statefulness.
MCP Cons
- Easy tasks for easy tools: even with an MCP, an AI agent can mess up by, for example, not accounting for pagination. Meanwhile, a deterministic API will always go through the data exactly as ordered.
- Too many tools: an MCP server that exposes too many tools gives too much food for thought for the AI, meaning it may burn through tokens just by considering what tool to use. The industry is working on solutions, like Bright Data introducing tool groups for their MCP, or Programmatic Tool Calling by Claude.
MCP vs. API: A Table
Here’s final tally of the pros and cons, as well as some recommendations that arise from it:
In Conclusion
While API as a concept predates MCP, it doesn’t mean that it’s outdated or useless when building AI agents. As is often the case, there’s a right tool for the right task.
If the task calls for large-scale data handling, speed, or accuracy of detail, then an API is the best choice.
If automation, scalability, and ease of integration are what you need, then you should use an MCP.
In terms of web scraping, you can take a look at the list of the best web scraping APIs and check our list of best MCP servers for web scraping.
