MCP vs API

MCP vs API: Simplifying AI Agent Integration with External Data (IBM)

Bottom Line MCP provides a consistent, AI‑native protocol that simplifies connecting LLMs to diverse external data and tools, reducing bespoke code and increasing adaptability.

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Transcript Summary

Need for External Integration LLM‑based applications must interact with outside data and services. Historically this was done through ad‑hoc APIs.

Model Context Protocol (MCP) Introduced late 2024 by Anthropic. Acts like a universal “USB‑C port” for AI apps, standardizing the way an LLM connects to tools and data.

Architecture

• MCP Host runs one or more MCP clients.

• Each client opens a JSON‑RPC 2.0 session to an MCP server.

• Servers expose capabilities (tools, resources, prompt templates) that the AI can query and use at runtime.

Core Primitives

1. Tools – callable actions/functions (e.g., get_weather, create_event).

2. Resources – read‑only items or documents (files, DB rows).

3. Prompt Templates – reusable prompt snippets.

• What MCP Solves

  • Supplies context (documents, records) to the model.

  • Lets AI agents execute external actions in a uniform way.

  • Provides machine‑readable catalogs, enabling dynamic discovery without redeploying code.

MCP vs. Traditional APIs

Aspect	MCP (purpose‑built)	Traditional API (general)
Target use‑case	LLM / AI agents	Any client–server integration
Discovery	Built‑in runtime capability listing	Usually none; manual docs
Interface	Single, standardized schema & calls	Unique per service
Adaptability	Agents auto‑adapt to new tools	Client code must be updated

Complementary, Not Competing Many MCP servers wrap existing REST or other APIs internally—MCP is an abstraction layer on top.

Analogy Recap Laptop = MCP host · USB‑C cables = MCP protocol · Peripherals = MCP servers (DB, repo, email, etc.).

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