A Complete Guide to the Model Context Protocol (MCP) in 2025

This is a very long article, estimated 15–20 minutes reading. Please save it for later. After you read this article, you will be the expert of MCP.

Introduction

Large language models (LLMs) have become incredibly powerful, but they often operate in isolation. One of the biggest challenges in developing AI applications is giving these models the context they need from external data sources (documents, databases, APIs, etc.) in a reliable and scalable way. Traditionally, each new integration between an AI assistant and a data source required a custom solution, creating a maze of one-off connectors that are hard to maintain.

To address this, Anthropic (the team behind the Claude AI assistant) introduced the Model Context Protocol (MCP) in late 2024. MCP is a universal, open standard designed to bridge AI models with the places where your data and tools live, making it much easier to provide context to AI systems. In this blog, we’ll explore what MCP is, why it’s needed, how it works, and what it means for developers and the broader AI industry.

What is MCP?

An abstract illustration of different pieces of context (represented by various shapes) connecting to a central hub, symbolizing how MCP links diverse data sources to an AI model.

Model Context Protocol (MCP) is an open protocol that standardizes how applications provide context to AI models (particularly LLMs). In other words, it’s a framework that defines a common language for connecting AI assistants to external data sources and services. Anthropic aptly describes MCP as “like a USB-C port for AI applications” – a universal connector that lets AI models plug into various tools and databases in a consistent way. Just as USB-C standardized how we connect devices, MCP standardizes how AI systems interface with different data sources and functionalities.

The purpose of MCP is to break down the silos between AI models and the vast information they may need. It enables developers to set up secure, two-way connections between AI-powered applications and the systems where data lives. For example, with MCP, an AI assistant could retrieve a document from your knowledge base, query a database, or call an external API – all through a unified protocol.

This means AI applications are no longer “trapped” in isolation from company content or tools; instead, they can seamlessly access up-to-date information and context as needed. Ultimately, MCP’s goal is to help models produce better, more relevant responses by always having the right context on hand.

History and Development

MCP was developed by Anthropic and open-sourced in late 2024 as a response to a growing problem in the AI field. At the time, there was no common standard for integrating AI models with external data and services – every integration was bespoke and non-interoperable. This led to what Anthropic engineers call the “M×N problem,” referring to the combinatorial explosion of connecting M different AI models with N different tools or data sources. Each new pairing required custom code, making it difficult to scale and maintain AI systems in real-world applications.

Seeing this pain point, Anthropic designed MCP to standardize the interface between AI assistants and data sources. They announced MCP’s release in November 2024, providing a formal specification and SDKs (Software Development Kits) for developers, along with a set of reference implementations. From the start, MCP was conceived as an open-source project and open standard, encouraging collaboration from the community rather than being tied to a single vendor.

Early adopters quickly rallied around the idea. Companies like Block (formerly Square) and Apollo integrated MCP into their systems during its initial launch, while developer tool providers including Zed, Replit, Codeium, and Sourcegraph started working with MCP to enhance their platforms.

This early traction demonstrated the demand for a universal context protocol. As Block’s CTO put it, “Open technologies like the Model Context Protocol are the bridges that connect AI to real-world applications,” underscoring MCP’s role in making AI integration accessible and collaborative.

By releasing MCP as an open standard, Anthropic set it on a path similar to other successful tech standards (think of HTTP for web or SQL for databases). The development effort included not just Anthropic’s team but also community contributors. Today there are official SDKs in multiple languages (Python, TypeScript, and even Java/Kotlin) and a growing collection of open-source MCP servers built by the community for various popular systems. In summary, MCP’s development was driven by the necessity to simplify AI-data integration, and its open-source nature has spurred a collaborative ecosystem from the get-go.

Why Use MCP?

Why did the industry need MCP, and why might you want to use it in your projects? In short: providing context to AI models has been challenging and MCP offers an elegant solution to those challenges. Here are the key issues and how MCP addresses them:

• Fragmented Integrations: Before MCP, if you wanted an AI model to access, say, your Google Drive, customer database, and Slack, you’d likely implement three different plugins or connectors – each with its own API and quirks. This fragmentation meant a lot of duplicated effort and a higher chance of bugs or stale integrations. MCP replaces these custom pipelines with one standard protocol. You can plug any data source or service into the model using the same method, drastically simplifying development.
• Scaling and Maintenance: Custom integrations don’t scale well. Every time an API changes or you adopt a new AI model, you have to redo work. It becomes a maintenance nightmare over time. MCP was explicitly designed to solve this “many-to-many” integration problem. Using MCP, an AI tool and a data service that both support the protocol can work together out of the box, without additional glue code. This standardization means fewer broken links when things update, and easier scaling to new use cases.
• Context Quality and Relevance: Even the smartest AI is only as good as the information it has. Without easy access to fresh context, models might give generic or outdated answers because they’re “blind” to your current data. MCP helps ensure the model can always fetch relevant, up-to-date context when needed. For example, instead of a model guessing an answer from training data, it could call an MCP connector to your knowledge base and retrieve the exact information, leading to more accurate and relevant responses.
• Interoperability: In the rapidly evolving AI landscape, you might experiment with different LLM providers or tools. Without a standard, each switch means re-integrating data sources in a new way. MCP provides a vendor-neutral interface, so you could swap out the underlying model (say from Anthropic Claude to another AI system) or add a new tool, and as long as both sides speak MCP, the integration still works. This flexibility saves time and avoids vendor lock-in, which is a big win for both developers and organizations.
• Security and Control: Providing context often means giving an AI access to sensitive data. Many developers are (rightly) cautious about sending internal data to third-party services or into the wild. MCP was designed with security best practices in mind. Because it’s an open protocol, you can host MCP servers within your own infrastructure, keeping data behind your firewall. You expose only what’s needed through the protocol. MCP’s standardized approach also makes it easier to audit and enforce policies on how the AI accesses data (e.g. requiring certain authentication for certain data sources). In short, MCP lets you share context safely without sacrificing control over your data.

By addressing these challenges, MCP makes it much easier to build AI applications that are context-aware. Instead of wrestling with countless custom integrations, developers can focus on the core logic of their application and trust MCP to handle the context exchange in a consistent, secure way. This results in faster development cycles and more robust AI solutions.

Key Benefits of MCP

To summarize the advantages, here are some of the key benefits of using the Model Context Protocol in AI/ML applications:

• Improved AI Performance and Relevance: Because the model can easily access the information it needs, it can deliver more accurate, context-rich answers. Anthropic designed MCP with the aim of helping “frontier models produce better, more relevant responses” by breaking down data silos. Early adopters have found that giving AI assistants direct access to relevant data (via MCP) leads to more nuanced and correct outputs—for example, AI coding assistants producing more functional code with fewer attempts when they can pull in project-specific context.

• Interoperability Across Systems: MCP is an open standard, not tied to any single AI vendor or data platform. This means it can act as a common bridge between diverse systems. An MCP-compliant data source can serve context to any MCP-enabled AI client, and vice versa, akin to plugging any device into a universal port. This fosters a rich ecosystem where tools and models from different providers can work together out of the box. It also future-proofs your integrations.

• Development Efficiency and Reusability: With MCP, developers no longer need to reinvent the wheel for each new integration. You can build against a standard protocol once and reuse that across projects. There’s also a growing library of pre-built MCP connectors (servers) for popular services like Google Drive, Slack, GitHub, databases, and more. You can simply plug these in, rather than writing custom code.

• Modularity and Scalability: MCP encourages a modular architecture for AI systems. By decoupling the AI model from the data sources via a well-defined protocol, each component can be scaled or upgraded independently. Need to add a new data source? Just spin up a new MCP server for it. Want to use multiple AI models in tandem? They can share the same context sources through MCP. It enables composable AI agents—mix and match capabilities like Lego blocks.

• Enhanced Security and Compliance: MCP supports keeping data within your infrastructure, only exchanging what’s needed through controlled channels. You can run MCP servers locally or in your cloud, secure with authentication and encryption. This helps meet data privacy regulations while still allowing powerful AI-driven functionality.

In essence, MCP offers a win-win: better performance and capabilities for AI models, and improved efficiency, flexibility, and safety for developers and organizations.

How MCP Works (Overview of Implementation)

So how does one actually use the Model Context Protocol? At a high level, MCP follows a client-server architecture to connect AI models with external context. Here’s a simplified overview:

• MCP Servers (Data/Tool Connectors): An MCP server interfaces with a specific data source or service, exposing it via the MCP standard. For example, servers exist for Google Drive, Git, GitHub, SQL, and Slack. These are often open-source and customizable.

• MCP Clients (in AI Applications): The AI application includes an MCP client that connects to MCP servers and relays context/data to the AI model. Claude’s desktop app includes this by default.

• Standardized Actions (Primitives):

  1. Prompts – instructions or templates the model can use.
  2. Resources – structured documents the model may read.
  3. Tools – functions the model can execute.

• Client-side Primitives:

  1. Roots – entry points (like a file folder or DB realm).
  2. Sampling – lets a server ask the model a sub-question.

• Integration Workflow: Developers typically run an MCP server for the data they want accessed and use SDKs to wire the client-side. This abstracts JSON-RPC and supports many environments.

• Developer Experience: The SDKs (in Python, TypeScript, etc.) simplify MCP implementation. Claude can even help write MCP code. It’s open, extendable, and dev-friendly.

Future of MCP and Its Impact on the AI Industry

MCP is still young, but its roadmap is ambitious:

• Wider Adoption: More AI tools, platforms, and vendors adopting MCP could standardize integrations across the industry. This means plug-and-play compatibility between LLMs and tools.

• Remote and Cloud Integration: Secure internet-based MCP use is growing—cloud-hosted MCP servers, remote AI agents, and enterprise hubs are on the way.

• Multimodal Support: MCP could eventually handle image, audio, and video context. Future AI could pull diagrams, recordings, or renderings into its context via MCP.

• Advanced Agentic Workflows: With MCP’s support for task decomposition and multi-agent collaboration, AI could autonomously coordinate tool usage across complex jobs.

• Standardization Influence: If MCP gains widespread traction, other tools may adopt it natively—e.g., CRMs or PM tools offering MCP endpoints for seamless AI access.

• Community-Driven Governance: Anthropic plans open governance for MCP—potentially forming an independent standards body. Community input will shape the protocol’s evolution.

Conclusion

The Model Context Protocol is an exciting development in the AI world because it tackles a very pragmatic problem: how to connect powerful AI models with the wealth of external knowledge and tools they need to be truly useful. By providing a common protocol for context, MCP makes it easier for developers to build intelligent applications that can see and act beyond their built-in training data.

In this blog, we introduced MCP, looked at why it was created, the benefits it offers, how it works, and where it’s headed. For developers and tech enthusiasts, MCP represents a big step toward AI that’s more connected, versatile, and collaborative.

As the standard gains adoption, we can look forward to a future where hooking up an AI model to a new data source is as simple as plugging in a device – and where the AI systems around us become ever more integrated and context-savvy thanks to innovations like MCP.