Unlocking AI Potential: The Model Context Protocol (MCP) Revolution

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Mukund Murali
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2 min read

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What is MCP ?

Model context protocol (MCP)

  • It is a protocol that allows LLMs to access custom tools and services.

  • If your LLM wants to talk to an agent for a specific task then with MCP it is easy to integrate the LLM to this Agent.

Why MCP is needed ?

MCP solves the following challenges in the current way of connecting LLMs to AI Agents:

  • Vendor lock-in: LLMs are tied to specific AI Agent providers, making it difficult to switch providers.

  • Security: LLMs need to access data and services which can lead to security issues.

  • Integration complexity: LLMs need to integrate with different AI Agents, which can be complex and time-consuming.

General Architecture

  • MCP Hosts: Programs like Claude Desktop, IDEs, or AI tools that want to access data through MCP

  • MCP Clients: Protocol clients that maintain 1:1 connections with servers

  • MCP Servers: Lightweight programs that each expose specific capabilities through the standardized Model Context Protocol

  • Local Data Sources: Your computer’s files, databases, and services that MCP servers can securely access

  • Remote Services: External systems available over the internet (e.g., through APIs) that MCP servers can connect to

MCP Example :

🧠 MCP Data Interface for OCI Object Storage

This project is a Model Context Protocol (MCP)-based API interface for Oracle Cloud Infrastructure (OCI) Object Storage. It allows AI agents, tools like Ollama, and natural language interfaces to programmatically list and read objects from OCI buckets via structured JSON requests.

📌 Features

  • ✅ List objects in an OCI bucket

  • ✅ Read and return file contents from a bucket

  • ✅ Minimal and easy-to-extend MCP-style API

  • ✅ FastAPI-powered Python server

  • ✅ CLI-driven backend using oci os commands

📦 Use Cases

This MCP server is ideal for:

  • AI/LLM agents needing data from OCI buckets

  • Streamlit or LangChain apps accessing cloud storage

  • FinOps or Security tools fetching bucket inventories

  • Conversational UIs querying cloud resources

  • Cloud-native notebooks fetching .csv, .json, etc. for ML

Read more and get the source code from my Github repo :

MCP