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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 interoperability fosters a rich ecosystem where tools and models from different providers can work together out of the box. It also future-proofs your integrations – if you switch to a new model or platform, you don’t need to rebuild your context pipeline from scratch.
• 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. This standardization and reuse significantly speed up development and reduce maintenance overhead. Teams can focus on the unique features of their application, rather than boilerplate integration 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, without touching your AI app’s core logic. Want to use multiple AI models in tandem? They can share the same context sources through MCP. This modular design makes complex AI workflows easier to manage and extend. It also enables composable AI agents – you can mix and match capabilities by connecting different MCP servers, essentially snapping together new functionalities like Lego blocks.
• Enhanced Security and Compliance: As noted, MCP was built with an eye on security. The protocol supports keeping data within your own infrastructure and only exchanging data through controlled channels. This means sensitive information doesn’t need to be exposed to the AI vendor’s servers if you run the MCP server locally or in your cloud. MCP can be configured to use secure authentication and encryption for these interactions (indeed, adding standardized auth support is a priority in ongoing MCP development). All of this helps organizations maintain compliance with data regulations and protect privacy, while still leveraging AI capabilities on their proprietary data.

In essence, MCP offers a win-win: better performance and capabilities for AI models, and improved efficiency, flexibility, and safety for developers and organizations. By adopting MCP, one can build AI solutions that are both more powerful and easier to maintain.

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 of how it works and how developers can integrate it into their AI workflows:

• MCP Servers (Data/Tool Connectors): An MCP server is a lightweight program that interfaces with a specific data source or service, exposing its capabilities through the MCP standard. For example, you might have an MCP server for a file system, one for a database, another for a SaaS tool like Slack or Notion. Each MCP server knows how to fetch data from or perform actions on its respective service, and it communicates using MCP’s defined message format. Anthropic and the community have already provided many open-source MCP server implementations (for Google Drive, Git, GitHub, Slack, SQL databases, etc.), so developers can often use or adapt an existing server instead of writing one from scratch.
• MCP Clients (in AI Applications): On the other side, an AI application (the host where the model resides) includes an MCP client component. This client is responsible for connecting to one or more MCP servers and relaying information between the AI model and those servers. For instance, Claude Desktop (Anthropic’s AI assistant app) has a built-in MCP client that can connect to any compatible MCP server on your machine or network. Similarly, other AI platforms or agent frameworks can implement MCP clients. The client sends requests to servers (like “get me this document” or “execute this tool function”) and returns the results back to the AI model in a format it can use. Communication is done through simple JSON-based messages (MCP uses JSON-RPC under the hood for request/response messaging), making it language-agnostic and developer-friendly.
• Standardized Actions (Primitives): MCP defines a set of core actions – often called primitives – that cover common ways an AI might need to interact with external context. On the MCP server side, there are three main types of primitives:

1. Prompts: Pre-defined instructions or templates that an AI can request (useful for providing situational instructions or formatting, for example).
2. Resources: Structured data or documents the server can send to the AI (for instance, a knowledge base article or a database record that can be inserted into the model’s context).
3. Tools: Executable functions that the AI can invoke to perform actions or fetch information (e.g. a function to query an API, send an email, or run a calculation).

On the MCP client side, the protocol defines:

1. Roots: Entry points that give servers access to certain data realms on the client side (for example, a root might grant a server access to a specific folder of files or a particular database).
2. Sampling: A mechanism that lets an MCP server request the AI model to generate a completion (essentially asking the model a sub-question or performing an intermediate reasoning step). This is a powerful feature for building complex agent behaviors – e.g., an MCP server can momentarily turn around and ask the AI to summarize something or make a decision as part of fulfilling a request. (It’s recommended to keep a human in the loop for such cases, to maintain control.)

• Integration Workflow: For developers, integrating MCP typically means either running an MCP server for the data you want your model to access, or using an AI tool that supports an MCP client (or both). For example, if you have an existing AI chatbot and you want it to access internal documents, you could deploy an MCP server that interfaces with your document repository, then modify your chatbot to include an MCP client (using Anthropic’s SDKs) that connects to that server. If you’re using Claude’s desktop app, integration might be as simple as installing a pre-built server plugin and giving Claude permission to use it.
• Developer Experience: MCP is meant to be developer-friendly. The open-source SDKs handle much of the boilerplate. For instance, the Python SDK can quickly spin up an MCP server by defining a few functions and annotating them as tools or resources. Anthropic even noted that Claude’s own LLM (Claude 3.5) is proficient at helping write MCP server code when prompted. On the client side, the SDK abstracts the JSON-RPC calls so you can work with normal function calls or event handlers in your code. Additionally, because MCP is open, developers are free to implement it in any language or environment – it’s not limited to Anthropic’s tools. This means you can integrate MCP into web apps, mobile apps, IDE plugins, or backend systems as needed.

Future of MCP and Its Impact on the AI Industry

MCP is a young standard, but it has ambitious goals and the potential to significantly shape how AI systems are built in the coming years. Here’s a look at what’s on the horizon for MCP and how it might impact the wider AI industry:

• Wider adoption and ecosystem growth: For MCP to realize its vision, it needs to be adopted beyond just Anthropic’s ecosystem. The signs are promising – with many companies and open-source projects already embracing MCP, we can expect more AI providers and tool developers to join in. Anthropic has positioned MCP as a truly open standard, even discussing possible formal standardization through an industry body in the future. This means that potentially multiple AI platforms (Claude, ChatGPT, open-source LLMs, etc.) and enterprise software providers could converge on MCP as a common interface. If that happens, the AI industry would gain something it sorely lacks: a universal interoperability layer. Imagine being able to use one AI assistant to interact with services from many different vendors seamlessly, or plugging a new AI model into your stack without having to rewire all your data integrations – MCP could make that a reality.
• Remote and Cloud Integration: Currently, a lot of MCP usage (especially with tools like Claude Desktop) happens on a local machine or within a controlled environment. A big focus moving forward is enabling secure remote connections via MCP. This involves building in robust authentication, authorization, and service discovery mechanisms so that an AI client could, for example, connect to an MCP server hosted in a cloud environment or on an enterprise network over the internet. In practical terms, this would allow organizations to deploy MCP servers alongside their web services or data warehouses and have AI models access them from anywhere in the world, with proper permissions. This could greatly expand the reach of MCP, making it suitable for cloud-based AI deployments and multi-user enterprise scenarios (where dozens of AI agents might connect to a central MCP service hub).
• Richer Modalities and Capabilities: So far, MCP has mostly been used for text-based context and tools (since it was born out of LLM use cases). But the standard could evolve to support additional data modalities – such as images, audio, or video – as first-class citizens. This means an AI model could request an image from an MCP server (say, a diagram from a knowledge base) or an audio clip (perhaps a recorded meeting) to use as context in a conversation. It also means tools for handling those modalities (for instance, an image recognition tool or a text-to-speech generator) could be standardized via MCP. As AI models become multimodal (able to process text, vision, and sound together), MCP might provide the unified pipeline to feed all those different types of context to the model in a structured way.
• Advanced Agentic Workflows: MCP opens the door for more complex AI agent workflows, where multiple steps or multiple agents are coordinated. The roadmap for MCP hints at supporting hierarchical agent systems and interactive workflows. In the future, we might see an AI “agent” breaking down a task into sub-tasks and spinning up different MCP tool calls (or even invoking other specialized AI models via MCP’s sampling calls) to accomplish an objective. For example, imagine an AI assistant that, given a high-level request, uses MCP to query a database, then calls a computational tool for analysis, then perhaps asks another AI model for a cross-check, and finally compiles the answer – all autonomously. MCP could serve as the common thread orchestrating these multi-step, multi-system processes. This could greatly enhance what AI systems can do, moving from single-turn Q&A to carrying out complex jobs with minimal human intervention (but still under human oversight as needed).
• Impact on AI Industry Practices: If MCP (or a protocol like it) becomes standard, it will influence how people design AI software. Developers might start building applications with AI integration in mind from the outset, knowing there’s a clear plug-in mechanism for context. Software providers might offer MCP endpoints with their products (imagine your CRM or project management tool coming with an MCP server interface out-of-the-box, so your AI assistant can hook into it immediately). This standardization could accelerate the trend of AI-enabled applications across industries, as the barrier to integrate AI with existing systems gets lower. It also encourages a more open, collaborative AI ecosystem: companies might contribute connectors and improvements back to the MCP community instead of keeping proprietary integration code, because a shared standard benefits everyone. Ultimately, MCP’s success could make AI more ubiquitous and useful in everyday technology, by ensuring that AI systems are context-aware by default rather than standalone black boxes.
• Community and Governance: Anthropic has emphasized that MCP’s future will be shaped in collaboration with the community. This means developers and organizations using MCP have a voice in its evolution. The protocol may be refined over time (with new primitives or features) based on real-world needs and feedback. There’s even the possibility of an independent governance model or standards body adoption to ensure MCP remains open and balanced among all stakeholders. For the AI industry, this model of open governance could serve as a blueprint for how to handle other AI-related standards in the future, promoting interoperability and safety through collective effort rather than siloed competition.

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.