What Is the Model Context Protocol (MCP) and Why Should Your Business Care?

The Model Context Protocol (MCP) is an open standard for connecting AI models to external tools, databases, and data sources. Created by Anthropic in November 2024 and now governed by the Linux Foundation, MCP has been adopted by virtually every major AI platform: OpenAI, Google, Microsoft, and thousands of developers building AI applications. It solves one of the most expensive problems in enterprise AI: the integration fragmentation that forces businesses to build custom connectors for every AI-model-to-tool combination. For businesses investing in AI, MCP is becoming the infrastructure standard that determines how your AI systems connect to the rest of your technology stack.

The Problem MCP Solves

To understand why MCP matters, you need to understand the problem it addresses.

Before MCP, every time you wanted to connect an AI model to a tool or data source, you needed a custom integration. If you wanted Claude to access your CRM, you built a custom connector. If you also wanted GPT-4 to access that same CRM, you built a different custom connector. If you later wanted to add your database, your email system, and your project management tool, you multiplied the problem.

Anthropic described this as an "N times M" integration problem. With N AI models and M tools, you needed N times M custom connectors. Each connector had its own implementation, its own maintenance burden, and its own potential failure points. This approach was expensive, fragile, and created deep vendor lock-in because switching AI models meant rebuilding all your integrations.

MCP replaces this with a standardized protocol. Build one MCP server for your CRM, and any MCP-compatible AI model can connect to it. Switch from one AI model to another, and your integrations continue to work. Add a new tool, and every AI model in your stack can access it through the same standard interface.

The analogy that works best: MCP is to AI what USB was to computer peripherals. Before USB, every device needed its own proprietary connector. USB created a universal standard, and the ecosystem exploded. MCP is doing the same thing for AI integrations.

How MCP Works (In Plain Terms)

MCP uses a client-server architecture. The core components are straightforward even if you are not technical.

MCP Host. This is the AI application, like Claude, ChatGPT, Cursor, or a custom AI application your business builds. The host is where the user interacts with AI.

MCP Client. This is a component inside the host that manages connections to MCP servers. When the host starts up, its client discovers what tools and data sources are available.

MCP Server. This is a lightweight program that exposes a specific tool, database, or service to AI models through the MCP standard. An MCP server for your CRM, for example, would expose operations like "look up a contact," "create a new deal," or "update a record."

When an AI model needs to access a tool, the interaction follows a standardized flow:

The MCP client connects to the server and asks what capabilities are available.

The server responds with a list of tools, resources, and prompts it offers.

When the AI model decides to use a tool, the client sends a standardized request to the server.

The server executes the action and returns the result.

The AI model incorporates the result into its reasoning.

All communication uses JSON-RPC, a widely supported protocol, and servers can communicate via standard input/output (for local connections) or HTTP with Server-Sent Events (for remote connections).

What MCP servers expose

MCP servers provide three types of capabilities:

Tools. These are actions the AI can take, like querying a database, sending an email, creating a file, or calling an API. Tools can have side effects and are the mechanism through which AI agents take actions in the real world.

Resources. These are data the AI can read, like database records, documents, or configuration files. Resources are read-only and give the AI context without modifying anything.

Prompts. These are reusable templates that help structure how the AI interacts with the server. They encode best practices and workflows specific to the tool or data source.

Who Has Adopted MCP

The adoption timeline tells a clear story about MCP's trajectory from experiment to industry standard.

November 2024: Anthropic introduces MCP and open-sources it.

March 2025: OpenAI officially adopts MCP, integrating it across its products including the ChatGPT desktop app.

April 2025: Google DeepMind confirms MCP support in Gemini models.

May 2025: At Microsoft Build, GitHub and Microsoft join MCP's steering committee. Hugging Face, LangChain, and Deepset integrate MCP into their developer frameworks.

December 2025: Anthropic donates MCP to the Agentic AI Foundation (AAIF), a directed fund under the Linux Foundation, co-founded by Anthropic, Block, and OpenAI. This move signals that MCP is no longer a single company's project but an industry-governed standard.

According to Pento's year-in-review analysis, the ecosystem has grown to over 10,000 active public MCP servers covering everything from developer tools to Fortune 500 deployments. Official SDKs in Python, TypeScript, Java, and C# have accumulated over 97 million monthly downloads.

MCP has been adopted by ChatGPT, Cursor, Google Gemini, Microsoft Copilot, Visual Studio Code, and numerous other AI products. Early enterprise adopters including Block and Apollo have MCP integrations in production.

Why Your Business Should Care

If you are building or deploying AI systems, MCP affects your strategy in several concrete ways.

Reduced vendor lock-in

Without MCP, your AI integrations are tied to a specific AI model. If you build custom connectors for Claude and later decide that GPT-4 or Gemini is a better fit for certain use cases, you rebuild your integrations. With MCP, your integrations work with any MCP-compatible model. This gives you negotiating leverage with AI providers and the freedom to use the best model for each task.

For more on choosing the right AI model, see our post on choosing the right AI model for your business.

Lower integration costs

Building custom AI integrations is expensive. Each connector requires development, testing, maintenance, and monitoring. MCP replaces N times M custom integrations with N plus M standardized ones: one server per tool, and one client per AI model. For a business connecting three AI models to ten tools, that is 13 integration points instead of 30.

Future-proofed AI investments

As new AI models and tools emerge, MCP ensures they can connect without custom development. An MCP server you build today for your internal database will work with AI models that do not exist yet, as long as those models support MCP. Given the current adoption trajectory, that is a safe assumption.

Enables AI agents

AI agents, systems that take autonomous actions rather than just generating text, need reliable ways to interact with tools and data sources. MCP provides the standardized infrastructure for those interactions. An agent built on MCP can query your CRM, update your project management tool, check your calendar, and send emails through a consistent interface rather than requiring fragile custom code for each system.

For more on how AI agents work and when you need one, see our post on AI agents explained.

Reduced hallucinations

MCP helps reduce AI hallucinations by providing a clear, standardized way for AI models to access external, reliable data sources. When an AI model can query your actual database for a customer's order status rather than guessing from its training data, the response is grounded in reality. This is particularly important for business-critical applications where accuracy is non-negotiable.

Practical Implications for Your AI Strategy

If you are planning AI projects in 2026, MCP should influence your decisions in several ways.

Ask about MCP compatibility when evaluating AI tools. Whether you are selecting an AI platform, a development framework, or a service provider, ask whether they support MCP. In 2026, MCP compatibility is a strong signal of technical maturity and future viability.

Build new integrations on MCP. If you are connecting AI to your internal systems, build MCP servers rather than custom point-to-point integrations. The upfront effort is similar, but the long-term value is significantly higher because MCP servers are reusable across models and applications.

Evaluate your existing integrations. If you have already built custom AI integrations, consider migrating them to MCP. The investment pays off in reduced maintenance, improved interoperability, and the ability to swap AI models without rebuilding.

Consider MCP in your data architecture. MCP servers can expose your data warehouse, your APIs, and your business applications to AI models in a controlled, standardized way. If you are building or upgrading your data infrastructure, planning for MCP access from the start avoids retrofitting later.

Think about security and governance. MCP provides the mechanism for AI to access your systems, but you need to control what it can access and what it can do. Implement authentication, authorization, and audit logging on your MCP servers. The standard supports these concerns, but the implementation is your responsibility.

What MCP Does Not Solve

MCP is an important piece of infrastructure, but it is not a complete solution for every AI integration challenge.

MCP does not choose which AI model to use. It makes it easier to switch between models, but you still need to evaluate which models are best for your specific use cases.

MCP does not solve data quality. If your underlying data is messy, inconsistent, or incomplete, exposing it through MCP just gives AI faster access to bad data. Data quality is a prerequisite, not a byproduct, of good AI integration.

MCP does not eliminate the need for engineering. Building MCP servers requires understanding both the tool you are exposing and the protocol itself. The standard reduces the amount of custom code, but it does not eliminate it.

MCP does not guarantee security. The protocol provides hooks for authentication and authorization, but securing your MCP deployment is your responsibility. An improperly secured MCP server could give AI models unintended access to sensitive systems.

Key Takeaways

MCP is an open standard for connecting AI models to tools and data sources, replacing fragile custom integrations with a universal protocol.

It was created by Anthropic, adopted by OpenAI, Google, and Microsoft, and is now governed by the Linux Foundation. The ecosystem includes over 10,000 active servers and 97 million monthly SDK downloads.

For businesses, MCP reduces vendor lock-in, lowers integration costs, future-proofs AI investments, and enables AI agents to interact with your systems through a consistent interface.

MCP is the infrastructure layer that makes AI agents practical. Agents need standardized ways to use tools and access data, and MCP provides exactly that.

Build new AI integrations on MCP. If you are evaluating AI tools, ask about MCP compatibility. It is becoming the standard that determines how well your AI systems play with everything else.

Frequently Asked Questions

What is the Model Context Protocol (MCP)?

MCP is an open standard that provides a universal way for AI models to connect to external tools, databases, and services. Think of it as a USB standard for AI: instead of building a custom connector for every AI-to-tool combination, MCP provides one standardized interface that any AI model can use to interact with any compatible tool or data source.

Who created MCP and who uses it?

Anthropic created MCP and open-sourced it in November 2024. In December 2025, Anthropic donated MCP to the Agentic AI Foundation under the Linux Foundation, co-founded with Block and OpenAI. It has been adopted by ChatGPT, Google Gemini, Microsoft Copilot, Cursor, Visual Studio Code, and thousands of developers, with over 10,000 active public MCP servers.

Why does MCP matter for businesses?

MCP reduces vendor lock-in by letting businesses switch AI models without rebuilding integrations. It lowers integration costs by replacing custom connectors with a standard protocol. It future-proofs AI investments because any MCP-compatible tool works with any MCP-compatible model. And it enables AI agents that can interact with your existing business systems through a consistent interface.

How does MCP relate to AI agents?

MCP is the infrastructure layer that makes AI agents practical. Agents need to interact with tools, databases, and APIs to take actions. MCP provides the standardized protocol for those interactions, so an agent can query your CRM, update your database, and send emails through a consistent interface rather than requiring custom integrations for each system.

Is MCP ready for production use?

Yes. MCP has moved beyond experimental status with adoption by major AI platforms including OpenAI, Google, and Microsoft. There are over 10,000 active public MCP servers and official SDKs in Python, TypeScript, Java, and C# with over 97 million monthly downloads. Enterprise deployments are in production at companies including Block and Apollo.

MCP is becoming the standard infrastructure for how AI connects to everything else. At Vectrel, our custom AI development practice builds AI systems on open standards like MCP, ensuring your AI investments are interoperable, maintainable, and future-proof. Book a free discovery call to discuss how MCP fits into your AI strategy.