A huge number of companies are suddenly jumping on the Model Context Protocol bandwagon. What is it and what can you do with it?
In November 2024, Anthropic open-sourced its Model Context Protocol (MCP), “a standard for connecting AI assistants to systems containing data.” Since then, many companies have been using MCP to allow their AI systems to consult both local and external sources. It allows them to act with context.
In this article, we further explain what MCP means and what it actually does.
Model Context Protocol
MCP is an open protocol that allows AI models to communicate directly with other tools, databases, and applications, without needing to build a separate API for each integration.
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Until recently, most AI systems operated largely closed, using information they received via training data. That worked, but they couldn’t use recent information. MCP changes that by being a universal connection layer between AI and the outside world.
Popular AI companies like OpenAI and Claude are already using it. Their models can connect via MCP to external data sources such as databases or local files, search engines, or specialized prompts. On the MCP website, the protocol is described as “a USB-C port for AI applications.” A USB-C port is a standardized way to connect electronic devices, so MCP connects AI applications with external systems.
This significantly reduces headaches for developers. Previously, they had to develop integrations via APIs for each tool or source, but now that is completely eliminated. This makes AI, and especially AI agents, not only smart but also useful tools. That means faster development, lower costs, and fewer errors.
These advantages explain why so many companies are suddenly jumping on that bandwagon. Instead of having to adapt their AI system to new tools every time, they can now build on a single architecture.
How does it work?
The architecture behind MCP is essentially always the same. An MCP host, or an AI-powered app such as agents from Claude or ChatGPT, is connected to one or more MCP servers, each containing a different application or source. Some servers can access local sources like files or databases, while others communicate with APIs or online cloud services. This entire process falls under the umbrella of MCP.
An MCP server translates a user’s prompt into a command for a specific tool. For example, an Otter MCP server can transcribe audio recordings on demand. The server processes that request, delivers a result, and provides the AI with the context to continue working with the transcript.
Is MCP Unique?
The idea of giving AI systems access to external data is not new. Developers have long tried to provide AI models with external information. What makes MCP different is that it offers a single universal standard. It breaks the dependency on specific APIs and can be used by various systems.
For example, a Claude agent can use the same MCP server as a ChatGPT agent. This enables large-scale collaboration.
Advantages and Considerations
The advantages of MCP primarily lie in collaboration. Developers no longer need to write a separate API integration for each application. Companies can make local and cloud-based data available to their AI systems via the same protocol.
This makes it easier to, for example:
- search internal documents without sharing sensitive data externally
- combine data from different systems into a single context
- allow AI agents to work with up-to-date information instead of static knowledge
However, there are also considerations. Because MCP forms a bridge between AI and sensitive business data, security and access management remain crucial. Researchers have already found several vulnerabilities such as prompt injection or unauthorized execution of commands.
Where MCP Stands Now
MCP is currently in an early phase of adoption. The standard is being further developed by Anthropic and the open-source community. Analysts state that there is a high probability that MCP, or a variant thereof, will evolve into a standard for AI integration. If that happens, the AI landscape would completely change from closed chatbots to AI systems that actively collaborate with their existing software and data infrastructure.
The Model Context Protocol is an attempt to streamline the growing landscape of AI integrations. By introducing a single open standard that allows language models to communicate with other systems, MCP lowers the technical barrier for companies to deploy AI.