MCP .. Explained!

MCP -Model Context Protocol. What is it? Why has it created such a buzz? What should Product Managers and Leaders know about MCP? This article explains all you need to know about MCP.

What is MCP? Why has it created such a buzz? What should Product Managers know about MCP?

What is MCP?

MCP is an open source standard that Anthropic introduced in November 2024. MCP stands for Model Context Protocol and it is designed to package a structured context and to streamline how AI models, particularly Large Language Models (LLMs) like ChatGPT, Claude, or Gemini connect to all sorts of external tools, databases, and applications, without the need for custom coding or one-off integrations every time.

The MCP protocol allows you to:

• Define what context an AI Agent should have

• Use “Resources” (e.g. documents, APIs, functions) in a standardized manner

• Plug them into agents across providers like OpenAI, Anthropic, Mistral, etc.

Think of MCP as the “USB-C for AI integrations”. Just like USB-C lets you plug almost any device into your laptop, MCP lets your AI Assistant “plug into” different software systems, like your CRM, issue tracker, calendar, or analytics tools - all using a common language and set of rules.

How does MCP Work?

Client-Server Architecture: MCP uses a modular setup with the following components:

• MCP Clients: User facing AI interfaces (e.g., Claude Desktop, or ChatGPT or Google Gemini). Clients manage secure connections between hosts and servers.

• MCP Servers expose domain or Agent specific capabilities (data, tools, or prompts). They translate the AI’s requests into actions the tool understands, and send back results in a way the AI can use.

Components of MCP

An MCP server is a modular, focused integration layer that exposes tools, resources, and prompts to AI clients, while maintaining strict isolation and security. Here are the main components of MCP and their roles:

1. Tools: Invokable functions that the AI Agents /LLMs can call to perform specific actions (e.g., create a ticket, fetch orders, run a script).

• Each tool has a name, description, input schema (usually JSON), and output format. Tools enable the LLM to take actions or retrieve information from external systems through well-defined interfaces.

• Examples: fetch weather; process refund; book my ticket

2. Resources: Structured data objects or files the server exposes (e.g., file systems, logs, database records, API responses).

• Resources can be listed, read, or subscribed to for updates.They provide the AI LLMs with access to static or dynamic data needed for context or decision-making.

3. Prompts: Reusable prompt templates with named arguments and context bindings.

• Prompts can be used to guide the LLM in multi-step workflows or to standardize certain types of queries. Prompts allow users or the server to inject structured instructions or templates into the AI’s contex.

4. Memory Management: manages how the server stores, retrieves, organizes, and exposes information (memories) so that AI models can "remember" facts, preferences, or past interactions across sessions and tools.

• Effective memory management enables persistence, personalization and dynamic, context-aware responses or actions resulting in delightful customer experience.

5. Security and Isolation: MCP servers operate independently, only exposing selected capabilities and receiving limited context. They maintain strong security boundaries; servers cannot access the full conversation or see into other servers.

A Visual Example:

Why is MCP framework a big step forward?

With the world moving towards Agentic AI, LLMs and SLMs are expected not to just generate responses but act as autonomous, interactive systems, i.e. calling services or APIs, retrieving information, executing workflows, and making decisions. However, without a standard for these interactions between software systems and AI/LLM models, developers were left building custom integrations, leading to long Go-To-Market cycles, product development inefficiencies, and faced barriers to scaling.

MCP simplifies development, reduces duplicated efforts, and enables scalable, context-aware AI applications. However, its success is going to be dependent on widespread adoption, as competitors like OpenAI (Function Calling with Swagger) Open API), Microsoft (MCP for AzureAI) and Google (Vertex AI) pursue proprietary alternatives.

Building Agentic Workflows for Enterprise Use Cases

MCP will empower AI Agents to perform complex, autonomous tasks (e.g., orchestrating workflows across tools) while maintaining context, making it a step toward more independent AI systems. With this acceleration in Agentic AI development, Product Leaders must consider the following benefits and contingencies:

• Model-Agnostic Flexibility: Unlike proprietary solutions of the recent past, MCP is designed to work with any LLM, promoting AI Model provider flexibility and reducing lock-in.

• Security and Control: MCP prioritizes security with strict access controls, user consent for tool execution, and no need to share API keys with LLM providers.

• Data Governance and Privacy: AI Agents can access shared memory, though that puts extra scrutiny on data lineage, permissioning, and compliance. Each company using MCP must develop policies for which data is shared.

• Agent Lifecycle Management: Persistent, memory-driven agents require specialized management approaches, including tools for monitoring agent behaviors, debugging memory usage, auditing contexts, and resetting agents when necessary. MCP standardizes memory and context handling, simplifying the management of persistent agents.

• Multi-Agent Readiness: With multiple interacting Agents, Product Leaders must ensure infrastructure and governance support coordination, communication, conflict resolution, and shared memory. MCP streamlines inter-agent communication and coordination.

Benefits of MCP to Product Leaders

• No Coding Required: MCP is designed so you don’t need to be a developer to benefit. Once your organization enables MCP-compatible integrations, you can use natural language to get things done

• Less Context Switching: Product Managers often lose time switching between tools. With MCP, you stay focused—asking your AI to fetch data, create tickets, or summarize analytics without leaving your workflow.

• Build Smarter, Context-Aware AI: The AI can “see” and use real-time data from your tools, remember user context, and take actions, making it much more useful than a simple chatbot.

• Faster, More Adaptive Products: MCP lets you design products with dynamic and automated flows naturally, so your product can adapt to user needs and real-world context dynamically. I.e. with LLMs, your AI Agent can either execute the actions or it would ask users for input whenever necessary

Examples of MCP Server

• Personal Notes MCP Server: A developer creates a server to connect their note-taking app (e.g., Notion) to LLMs, allowing the AI to query or update notes.

• GitHub MCP Server: Someone builds a server that lets Claude or Gemini access a user’s GitHub repos, create pull requests, or commit code. The server handles GitHub API calls and translates them into MCP-compliant responses.

• Enterprise CRM MCP Server: A company builds a server to integrate its CRM system (e.g. Zendesk or HubSpot) with MCP, enabling Claude to fetch customer data or log interactions.

Summary

The Model Context Protocol (MCP) is a significant step forward towards the future of AI because it standardizes how AI systems connect to external tools and data sources, much like HTTP did for the internet by defining a universal language for web communication. Before MCP, AI models were isolated, requiring custom integrations for each new application, which slowed innovation. MCP acts as a "universal adapter," enabling AI to seamlessly interact with diverse systems—like databases, APIs, or creative software—through a consistent protocol, fostering interoperability and scalability. Just like invention of HTTP drove explosive growth of the World Wide Web, MCP has a solid potential to be the foundational layer for an AI-driven ecosystem.

In the next post, I will dive deeper into an application of MCP for a practical use case.