What is Model Context Protocol (MCP)?

12 April 2025, Saturday 09:52 , Written by: Bilus Teknoloji

As large language models (LLMs) become more powerful, their ability to interact with the real world is often limited by their static nature — they can’t access fresh data, execute custom logic, or adapt dynamically to different tasks. Model Context Protocol (MCP) is here to change that.

MCP is an open protocol developed by Anthropic designed to bridge LLMs with external tools, resources, and prompts in a standardized, scalable way.

Why MCP Matters

Traditional LLMs operate in a closed-box fashion. Once trained, their knowledge becomes stale and they can’t inherently “reach out” to query databases, call APIs, or use external files.

MCP provides a structured, language-agnostic interface to overcome that limitation.

• Tools: Execute external functions (e.g., search weather, run SQL query).
• Resources: Retrieve external files or documents.
• Prompts: Use task-specific templates to guide interaction logic.

This allows LLM-based apps to be:

• More dynamic
• More up-to-date
• More domain-specific
• More capable of reasoning with real data

Real-World Use Case

Let’s imagine you’re building a support chatbot for an insurance company.

With MCP, your LLM can:

1. Use a Tool to call a claim-status API.
2. Retrieve a Resource containing policy details as a PDF.
3. Use a Prompt that frames the conversation with empathy and legal caution.

Example:

{
    "tool": "getClaimStatus",
    "resource": "documents/policies/auto-2023.pdf",
    "prompt": "templates/support_response"
}

Instead of hallucinating or returning outdated info, the LLM combines context-aware inputs with real data.

How to Get Started

You can build MCP-compliant systems using official SDKs in:

• Golang
• TypeScript
• Python
• Java
• Kotlin
• C#

Start here 👉 github.com/modelcontextprotocol Or check the docs 👉 modelcontextprotocol.io

Model Context Protocol (MCP) redefines what language models can do by giving them structured, secure, and modular access to tools, data, and prompts.

It brings reasoning closer to real-world usage — and lets LLMs collaborate more meaningfully with the systems around them.

If you’re building AI-powered apps, MCP might just be the missing piece you’ve been looking for.