API vs. MCP: Everything you need to know

Your AI agents are as good as the tools they use. Current state-of-the-art LLMs are capable enough to use tools for real-world use cases. However, the high-quality data are usually gated behind applications like Salesforce, Google Sheets, Slack, GitHub, etc. And the only way LLMs can access those is through API endpoints, but building and maintaining integrations for third-party services can be a nightmare.

This was the reason MCP was introduced to standardise tool use for tool providers as well as consumers.

But do you even need it? Do these additional abstractions give any extra benefits? Why not just use the APIs directly? These are some of the fundamental questions everyone is asking now.

In this blog post, we will clear all these queries.

TL; DR

• APIs allow you to connect systems, but making them work for LLM agents takes heavy lifting like clear docs, schema handling, context management, and custom integrations.

• APIs fall short due to integration complexity, are unoptimized for LLMs, and lack standardisation for building AI apps.

• MCP was built to fix this by offering tool discovery, context/state handling, semantic feedback, fine-grained security, and session history.

• For MCP server builders, you’d still have to write integration logic for APIs, but only once, and these expose API actions/tools as discoverable primitives.

• If you’re a tool user, MCP makes life easier with auto-discovery, built-in context handling, and better error management.

• MCP tradeoffs include less flexibility for complex tasks (not ready for many real-world applications yet), scale limits, and the education around it is still amateur.

• Use APIs when you need flexibility, control, or legacy integration.

• Use MCP when building new agent-native products, fast onboarding, and efficient stateful workflows matter.

• The future is hybrid with APIs for power and MCP for simplicity.

Working With APIs: What It Takes

Traditionally, APIs (Application Programming Interfaces) have been the go-to choice for connecting software systems. But making an API work for an AI agent is more involved than just exposing an endpoint.

To make traditional APIs eligible for LLM-driven use cases, developers often have to:

• Improve tool descriptions: API docs primarily target human developers. For AI use, descriptions must be crystal-clear, precise, and machine-comprehensible, covering purpose, required parameters, expected outputs, and potential errors.

• Integrate security protocols: Implement OAuth or API key flows, sometimes customising scopes or permissions to prevent agents from overstepping their bounds and to ensure data safety.

• Handle schemas and input/output formats: APIs expect requests and responses in well-defined JSON or similar schemas. LLMs, by contrast, produce natural language, so translation layers are needed.

• Manual context management: Since APIs are stateless, the agent must remember the context (like ongoing tasks or previous outputs) and supply it with every request.

• Juggle state: If a task spans multiple steps (such as booking travel), the API itself is unaware of the background; each request is “memoryless,” which increases the risk of confusion or errors.

• Custom integration work: Connecting an LLM to a new API often involves substantial engineering, mapping prompts to endpoints, building special adapters for retries, handling edge cases, and anticipating vague human queries.

For a full breakdown of what it takes to adapt APIs for LLMs, dive into this field guide on how to build tools for AI agents.

Where APIs Fall Short with LLMs

Adapting APIs for use by LLM agents comes with various friction points:

• Rigid expectations: Parameters, endpoints, and inputs must be known in advance. If anything changes at the API, things break.

• Lack of context-awareness: APIs are built to be used in deterministic code workflows; LLMs workflows are non-deterministic. You need to go the extra mile in engineering tools for API endpoints optimized for LLMs, like Clear tool name, description, and parameter names. Also, you must handle memory and state by yourself.

• Token and cost inefficiency: Embedding all docs or prompts within each API call eats up tokens, bloating costs for every agent action.

• Generic error handling: HTTP status codes and abrupt messages (like 404, 500) aren’t helpful for AIs-they lack actionable, fine-grained feedback to guide self-correction.

Enter MCP: Why It Exists

MCP-Model Context Protocol was purpose-built to solve these issues for AI workflows. At its heart, MCP aims to transform the tool integration into something as seamless for LLMs as plugging devices into a USB-C port.

However, hold on, MCP is still built on the bedrock of traditional APIs; after all, that’s how it communicates with external applications.

How does MCP work?

LLMs know tool calling, and this is how they can select any tool or a function. Users provide detailed tool definitions, with an input parameter schema. The LLMs selects the tool to use based on the context of the task.

MCP standardises this concept; if you were building a GitHub tool, you would have to create a function and tool for each action, like fetching code from GitHub or adding comments to PRs. MCP standardises this anyone who has built a Gmail MCP server built by any person can be used by everyone with zero integration effort. For more on MCP, check out: Model Context Protocol Explained

Image source: Paragon

So, what basically MCP does is standardise how people integrate apps. But it’s not limited to apps, but files, Git, shell or any piece of code for that matter.

Here’s what MCP offers

• Dynamic discovery: Agents can ask, “What tools do you have?” MCP servers return a machine-readable list of available tools, resources, and prompts, with all their options and requirements spelt out.

• Contextual efficiency: Instead of stuffing every prompt with context, MCP handles relevant state and background data, including resources, history, and active tasks, making each call lighter and brighter.

• Semantic feedback: When things go wrong, MCP doesn’t just throw cryptic codes. Instead, it provides detailed, natural-language errors (like missing parameter names or value constraints) that an agent can understand and fix autonomously.

• Fine-grained security: Each tool or resource can have its own permission scopes. For example, a GitHub integration might allow only “create pull request,” “fetch notifications”,-and nothing else.

• Stateful conversations: MCP tracks session history, task state, and conversation context, making multi-step, agent-guided processes reliable and resumable.

Two MCP Perspectives: Server Builders & Users

If you’re an MCP server builder:

• All the groundwork of classical API development still applies-authentication, input validation, and endpoint logic-but you now also structure your services as discoverable “primitives” (tools, resources, prompts) according to the MCP protocol.

• You gain built-in benefits for context, error handling, and authorisation, reducing the tedious task of manually joining docs to endpoints or inventing custom “agent adapters”.

If you’re a tool user or AI product builder:

• You don’t need to worry about protocol details. Plug your agent into an MCP server; all available capabilities, docs, and permission scopes are fetched on demand.

• The heavy lifting—mapping prompts to functions, managing context, and error handling —is managed by the server provider.

Cons of MCP Versus APIs

MCP is not a magical solution - there are tradeoffs:

• Less flexibility for unique or legacy tasks: MCP works best when tasks map neatly to the protocol's primitives. Complex business processes or systems not designed for MCP may not fit cleanly.

• Still requires underlying APIs: Most current MCP servers wrap existing APIs, sometimes adding conversion work and overhead. As a result, performance can lag.

• Not “composable” at large scale: Binding multiple stateful or interdependent processes may hit protocol or design limits. Currently, most MCP clients (VS Code, Cursor, Windsurf, and Claude) impose a limit on the MCP server tools that can be active. However, you can use Rube - a universal MCP server that allows you to access only the tools you need.

• Early stage for advanced workflows: MCP shines with simple tasks and common agent actions. Demanding challenges, like real-time analytics or deep workflow automation, remain tricky. APIs are still better in this case.

When to Use APIs vs. MCPs?

• Choose APIs when:

  • Flexibility is key: The system or workflow is unique, highly customized, or not easily mapped to MCP primitives.

  • You need direct access and fine control over endpoints, parameters, or authentication for advanced engineering work.

  • Migrating existing, mature services is not feasible.

• Opt for MCPs when:

  • Building new AI agent products or integrating with standardized tools that support MCP out of the box.

  • You want fast onboarding, self-discovery, and efficient context handling for agents, without manual integration work.

  • Fine-grained permissions, dynamic adaptation, and state management are needed for long-running agent workflows.

Final Conclusion

In today’s AI-powered applications, both APIs and MCPs play crucial roles, each with distinct strengths and limitations.

The future is likely to be hybrid: APIs for raw power and flexibility, MCPs for agent-native efficiency and simplicity.

Rather than picking one or the other, choose the proper protocol for the problem / combine both protocols and let your AI agents do what they do best.