Artificial intelligence continues to gain popularity at a tremendous rate. In a short time, it has become an almost integral part of people's lives, from data analysis to creative content generation. After traditional and generative AI, the next wave has already emerged - Agentic AI. Unlike traditional LLMs that stop at generating text, Agentic AI is designed to interact with the world and carry out tasks. AI agents don't just analyse and respond to prompts; they can also decide and act.
Imagine you're asking an AI assistant to schedule a meeting with your team. It's great at generating a polite invitation message and even suggesting the best times. But when it comes to actually checking everyone's availability in your calendar system, sending out the invite, and reserving the meeting room, everything comes to a standstill. Without direct access to your tools, the assistant can only suggest what you should do, leaving you to carry out the steps manually.
This limitation points to a bigger issue with large language models (LLMs) - they're excellent at handling massive amounts of data, but they fall short when it comes to real-time context beyond their training. For AI agents to be able to act, they need more than just raw intelligence; they need dynamic access to files, tools, knowledge bases, and the ability to act on that information. Traditionally, connecting models to external sources required clunky, one-off integrations that were fragile and hard to scale. But there is a solution that has arrived to fill this gap, just as REST standardized API communication many years ago.
In this post, we will talk about Model Context Protocol (MCP), giving AI agents a standardized way to plug into tools, data, and services. We'll break down how MCP works, why it matters, and how it's transforming AI from a clever assistant into a truly autonomous agent.
What is MCP?
Model Context Protocol (MCP) is a universal standard that was designed to enable AI models to seamlessly communicate with external data sources for accessing real-time context, such as files, databases, or APIs — no hacks, no hand-coded workarounds. Instead of each AI system developing its own integration layer, MCP provides a common JSON-RPC 2.0-based protocol that any model, developer, or service can use. While MCP may sound technical, the basic idea is simple - giving AI agents a compatible way to connect to tools, services, and data, no matter where they are or how they are built. Think of it as a "USB standard" for AI—plug-and-play tools where models can instantly access new capabilities without having to be custom-developed for each environment.
MCP was introduced by Anthropic, the company behind Claude, in November 2024, and while MCP presents a major leap forward in how AI agents operate, eliminating numerous integrations, initially, it remained in the background of discussions about advanced language models. But in early 2025, MCP had come to the forefront, as it appeared that developing AI agents was just the beginning, while connecting them to real data was the real challenge, making the MCP approach a solution the industry had been looking for.
What’s the difference between MCP server tools and an API?
APIs are designed for apps. You send a request to a specific endpoint and get a set response. It works when you already know what you need and how to ask for it. For example, your weather app calls a weather API to get the forecast. Your finance app calls a bank API to fetch transactions. They're built for developers who know exactly what data they need and how to request it.
MCP is designed for AI models. MCP helps the model itself understand what tools exist, what they do, and how to use them. This way, the model can decide how to act.
It's less about "here's the endpoint, call it like this" and more about "here's a toolbox—use the right tool when you need it."
How MCP Works
MCP works by establishing a secure and efficient client-server architecture where AI systems request relevant context from data repositories or tools, with several key components:
MCP Host: An AI-based application (e.g., Claude Desktop, IDE, chatbot) that uses language models to perform tasks. The host manages interactions and decides when it needs to access external data.
MCP Client: The Intermediary that lives within the host app to ensure interaction between models and external tools. The client manages a stateful connection to a single MCP server. It is responsible for communication and negotiation of capabilities.
Server: An external program that provides access to tools (functions), resources (data), and queries (prompts), expanding the capabilities of AI models. Each server uses a standardized MCP interface, ensuring seamless integration.
When an AI application is launched, it creates MCP clients, each of which connects to a separate MCP server. They negotiate protocol versions and available capabilities. Once connected, the client asks the server for available tools, resources, and prompts. The AI model can access the server's data and functions in real time, dynamically updating its context. It means that MCP allows AI apps to work with the most current data, rather than relying on pre-indexed datasets, embeddings, or cached information in the LLM.
MCP architecture transforms the "M×N integration problem" (where M of AI applications need to connect to N tools, requiring M×N separate connectors) into a much simpler "M+N problem." So, it becomes just a plus problem, rather than a multiplication of integrations. As a result, each tool and application only needs to support the MCP once to enable interoperability. MCP eliminates the need for fragmented integrations by providing a standardized framework, making it a significant time saver for developers. Besides, it enables AI assistants not only to retrieve information but also to perform meaningful actions, such as updating documents or automating workflows, thereby bridging the gap between isolated intelligence and dynamic, context-aware functionality.
Core Building Blocks of MCP
MCP standardizes communication using a set of rules called primitives to define what information can be shared and what actions can be taken between clients and servers. There are five main primitives that define what clients and servers can offer each other. These primitives enable a two-way collaboration: AI models use external tools and data, while servers leverage AI capabilities, creating dynamic, Agentic AI.
When an MCP client establishes a session with an MCP server, the client can ask the server about its capabilities. The server responds with its capabilities, and the client shares its own, using these five primitives:
Prompts (user-controlled): These are pre-defined templates or instructions to use tools or resources in the most optimal way that are embedded in the AI context.
Resources (app-controlled): These are external data sources that LLMs can access, such as database records or files, similar to GET endpoints in a REST API.
Tools (model-controlled): These are functions (tools) that LLMs can call to perform specific actions, such as "write a record to the database", "send a message", "retrieve/search for info".
Root (client-controlled): These are defined locations in the host's file system or environment that a server can access and interact with. They set boundaries for server operations and let clients specify relevant resources and their locations.
Sampling (client-controlled): These are requests from servers for help from the AI when needed, such as generating a database query. It is helpful when the server's creators want to use AI but don't want to build their own AI system or rely on a specific AI model. This way, the server stays flexible, and the client keeps control over its own AI.
These primitives enable a structured communication flow:
Requests – sent from clients to servers when the model needs information or actions.
Responses – returned from servers back to clients.
Notifications – asynchronous updates from servers to clients when new information is available.
At its core, MCP standardizes how models and external systems exchange:
Capabilities (what tools or data sources are available)
Schemas (the inputs/outputs of those tools)
Context (what the model is trying to accomplish)
Permissions (what the model is allowed to access)
So, when an AI system needs something (like "get stock prices" or "fetch user account details"), it doesn't need to know the specifics of a custom API. It just queries via MCP, and the connected service responds in a consistent, structured way.
What are the transport mechanisms of MCP?
MCP uses transport mechanisms, like communication channels, that carry MCP's primitives to let AI apps (clients) and external systems (servers) share data and tasks. MCP supports two main transports:
StreamableHTTP for remote/distributed setups: The primary method, using standard web requests (HTTP) to connect to a server's URL. It can optionally include Server-Sent Events (SSE) for real-time updates, like live notifications in a chat app. It is great for cloud-based or distributed systems.
STDIO for local integration: Used when the AI and server are on the same device, like a laptop or CLI tool. It's perfect for local testing or apps accessing nearby files.
Benefits for Developers & Businesses
MCP represents a big leap forward in how AI agents operate. Instead of just answering questions, agents can now perform useful, multi-step tasks, such as retrieving data, summarizing documents, or saving content to a file. Previously, each of those actions required a custom API integration, hand-written logic, and ongoing developer effort. MCP replaces that with a plug-and-play protocol: agents send structured requests to any MCP-compatible tool, get results in real time, and can even chain multiple tools together, without needing to know the details of each tool’s API. In short, MCP replaces fragile, one-off integrations with a unified, real-time standard built for autonomous agents.
Here are the clear benefits MCP offer for developers and businesses:
Interoperability & Reduced Vendor Lock-in: Released open-source under the MIT licence, MCP is gaining support from AI leaders such as Anthropic, OpenAI, Google DeepMind, Microsoft, Replit, and Zapier. This broad adoption makes it more likely to become a universal standard that everyone can use across tools, apps, and AI platforms. For developers, it means a single integration can work across different AI providers, reducing reliance on any single vendor.
Faster Development: MCP eliminates the need to individually integrate with each external tool. Developers can focus on developing features rather than managing integrations. It dramatically reduces friction when building AI-powered products.
Extensibility: New tools and services can be plugged in without modifying the model itself. As long as both sides “speak MCP,” AI apps automatically gain new capabilities as new tools become available.
Reduced maintenance overhead: Traditional API integration breaks when external APIs change. MCP abstracts these changes, reducing maintenance work.
Security & Trust: MCP uses permissioning and sandboxing (e.g., via the Root primitive). Users can control which tools the model has access to and under what conditions, ensuring data stays secure.
Ecosystem for Developers
MCP’s ecosystem is growing day by day with more and more marketplaces and tools are appearing to help developers integrate AI into their apps with ease.
Here are some popular Marketplaces for easy integration of ready-to-use MCP servers and clients:
mcpmarket.com: A directory of plug-and-play MCP servers, using Tools and Resources primitives for instant integration.
mcp.so: An open-source repo of community-built MCP servers, ready to fork or customize.
There are also curated references and documentation, not full marketplaces, but still valuable for discovering integrations like Awesome MCP Servers (GitHub list). “awesome-mcp-servers” list is a community-maintained collection of useful MCP servers, clients, and related resources
The number of infrastructure tools to simplify building and running MCP servers is also growing. These tools provide frameworks, SDKs, or hosting that help developers actually create, deploy, and maintain servers. Think of them as the “DevOps layer” for MCP:
Mintlify, Stainless, Speakeasy can auto-generate MCP servers with minimal effort, speeding up development.
Cloudflare, Smithery can host and scale servers using StreamableHTTP/SSE.
Toolbase can manage keys and routing for local setups, securing access with the Root primitive.
Bottom Line
As AI systems mature, the ability to access dynamic, real-world data becomes ever more critical. The adoption of MCP marks a turning point in AI development by providing an open, standardized way to connect large language models to external tools and data sources. Backed by top AI labs and open-source communities, MCP is building a rich ecosystem of SDKs, connectors, and adopters and will be a foundation for next-generation AI apps. For the consumer, the shift will be subtle but powerful: AI will no longer give generic answers, it will act. It might rearrange meetings, remind invitees, scan inventory within stores and reorder stock, or even arrange holidays. For businesses, it means customers can accomplish more through simple conversation, while AI handles the details—streamlining operations, reducing manual work, and enabling smarter, more responsive services.
FAQ
What is MCP?
Model Context Protocol (MCP) is a standardized way for AI agents to connect to external tools, data, and services in real time, enabling them to act, not just generate text.
Why was MCP needed?
Before MCP, AI agents could access tools, but it required custom, one-off integrations for every system. MCP simplifies this with a universal, plug-and-play approach.
How does MCP differ from APIs?
APIs are designed for apps—they expect specific requests and return fixed responses. MCP is built for AI models, letting them understand what tools exist and decide how to use them.
What are MCP primitives?
Primitives are the core building blocks of MCP that define what can be shared or performed between clients and servers. Examples include Prompts, Resources, Tools, Root, and Sampling.
How do AI agents use MCP?
AI agents request context from MCP servers using a client. The server provides real-time data, tools, or prompts. Agents can then act on that information—updating files, automating workflows, or pulling structured data.
What transport mechanisms does MCP use?
MCP supports StreamableHTTP for distributed systems and STDIO for local setups, ensuring flexibility across environments.
Who already supports MCP?
Major AI labs like Anthropic, OpenAI, and Google DeepMind, along with companies like Microsoft, Replit, and Zapier, are already adopting MCP.
How does MCP enable Agentic AI?
Unlike traditional AI that only generates text, Agentic AI can act—e.g., scheduling meetings or updating files. MCP gives AI agents dynamic access to tools and data, enabling multi-step tasks like checking calendars, sending invites, or managing inventory through a single, standardized protocol.
Is MCP secure?
MCP uses permissions, sandboxing, and client-controlled access (e.g., via the Root primitive) to ensure only authorized tools and data are accessible.
How can developers integrate MCP into their apps?
Developers add MCP by including an MCP client in their app, connecting it to an MCP server. They can use existing servers from marketplaces like mcpmarket.com, mcp.so, or Cline’s MCP Marketplace, or create their own using tools like Mintlify, Stainless, or Speakeasy. For hosting and scaling, services like Cloudflare and Smithery can be used.
