Until recently, developers of AI models and applications regularly faced the problem of manually configuring integrations, which significantly complicated their connection and data exchange with external systems. A brilliant solution to this issue was an innovative open source technology called the Model Context Protocol (MCP). In this article, we will tell you what the new AI integration standard is, how it works, what its advantages are, and how you can use it.

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What is Model Context Protocol (MCP)

MCP is a new open source protocol developed by Anthropic to standardize the interaction between language models (LLM) and third-party applications. Essentially, it is a unified interface through which AI models can connect to external tools and data sources. As the experts aptly noted, the MCP functions as a USB-C port, connecting LLM and AI agents to various software.

In particular, the Anthropic model context protocol allows integrating neural networks with many types of systems, including databases, content repositories, development environments, and various business applications. Thanks to it, developers no longer need to create integrations between AI models/agents and external software manually, study the API of each application, write code, and release updates. The new open source MCP standard is designed to standardize integrations, accelerating and simplifying the processes of their connection and data exchange.

Anthropic introduced the Model Context Protocol in November 2024. The new technology for developers includes three main components:

  • Specifications and SDK.
  • Support for local MCP servers in Claude Desktop applications.
  • Open source repository of MCP servers.

Claude's flagship Sonnet 3.7 model is perfectly optimized for integration with MCP servers, allowing companies and individuals using it to quickly and easily connect their datasets to high-performance AI tools. Additionally, Anthropic offers ready-to-use MCP servers for popular business applications, including Google Drive, Slack, GitHub, Git and more.

At the end of March 2025, it became known that another AI technology provider, OpenAI Corporation, was also going to implement the new standard. Furthermore, OpenAI's CEO Sam Altman has confirmed that the Agents SDK already includes support for the OpenAI model context protocol. This functionality will be expanded to the ChatGPT desktop application and the Responses API within the next few months.

Main Components and Principles of the MCP Protocol

In this section, we will talk about what are the main components of the model context protocol and how the new AI interface works. The system is structured around two fundamental components — MCP servers that provide access to various data sources, while MCP clients (AI applications) have the capability to connect to multiple servers simultaneously. Thanks to the described scheme, the software can interact with different AI tools and data sources without any restrictions.

MCP Architecture


The model context protocol architecture includes the following components:

  • MCP servers. Small programs, each of which provides clients with specific capabilities by connecting to local or remote data sources via the MCP standard.
  • MCP hosts. These are various applications or interfaces (e.g. Claude Desktop, AI tools or IDEs) that need access to resources via MCP. To achieve this, they send requests for the data or actions they require.
  • MCP clients. Systems that support one-to-one connections with MCP servers act as intermediaries for forwarding requests and responses.
  • Local resources. These encompass databases, files, and local computer services that MCP servers access through their namesake protocol. For example, they can send queries to SQLite databases, obtain permission to read documents, etc.
  • Remote resources. External systems to which MCP servers connect via Internet protocols (e.g. API). Through these components, AI models or agents can effectively communicate with cloud services or other tools.

The key characteristics of the MCP model context protocol are:

  1. Standardization. MCP is a standardized method for interaction between language models and AI agents with external tools and data sources, ensuring their full compatibility.
  2. Communication formats. The protocol supports several data transfer formats (including STDIO and SSE), which guarantees flexibility in integrating AI products with third-party software.
  3. Tool integration. MCP allows language models and agents to freely use external tools, which extends their functionality and application capabilities.
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How to Get Started With the MCP Protocol

To start using the capabilities of the Anthropic model context protocol, follow these steps:

1. Download and install the desktop application of the AI chatbot Claude on your computer. Alternatively, you can install ready-made MCP servers for popular data sources (Google Drive, Slack, GitHub, etc.)

2. If you chose the first option, add the Knowledge Graph Memory Server for Claude, which allows you to store and retrieve information. Check out the repo README for a list of available servers.

3. Open Claude settings and go to the “Edit Config” section. There, you need to add the MCP servers you need, for example, by entering the following code fragment:

{

"mcpServers": {

"sqlite": {

"command": "uvx",

"args": ["mcp-server-sqlite", "--db-path", "/path/to/your/database.db"] }}}

4. Using SDKs prepared by developers, you can create your own MCP servers, adapting them to specific tools or data sources.

5. Start a connection between your AI application and the MCP server to transfer data and perform actions via the model context protocol.

Participants shall adhere to the following principles when implementing the MCP standard:

  • Provide your applications with robust authorization mechanisms.
  • Provide detailed documentation describing security measures.
  • Implement appropriate access levels and data protection.
  • Follow current safety recommendations.
  • Consider privacy implications when designing features.

Advantages and Applications of the MCP Protocol

The widespread adoption of the model context protocol MCP provides a number of benefits for both the AI industry in general and application developers in particular.

Expanding the Capabilities of AI Products

The MCP standard allows language models to be reliably connected to a wide range of data sources. This speeds up the work of LLMs and improves the quality of the answers they generate, especially when solving problems in real time. The protocol can also transform AI agents from isolated chatbots into fully-fledged interactive systems deeply integrated into digital environments.

Simplified Integration

Previously, developers had to create custom solutions to integrate their software with AI models or agents, primarily by writing a lot of code manually. The emergence of the standardized MCP protocol has significantly simplified this process, which allows for faster development and reduced maintenance costs.

Enhanced Security

The Model Context Protocol provides enhanced security measures for data exchange and other actions between AI models/agents and third-party applications. MCP servers fully manage their resources, so developers do not have to pass API keys to AI product providers. The standard clearly delineates the capabilities and rights of participants, ensuring controlled and verifiable access to data.

Encouraging Interaction

The open source architecture of the MCP protocol encourages developers to actively collaborate with each other to develop and improve this standard. A convenient environment for collaboration stimulates innovation and expands the capabilities of the new data transfer format.

Among the key model context protocol use cases are:

  • Software development. The MCP standard allows developers to optimize their code generation tools by connecting AI models to code repositories or debugging services.
  • Data analysis. Integration of AI agents with databases or cloud storage via the MCP protocol will significantly increase their performance when performing tasks on analyzing large data arrays.
  • Process automation. With MCP, companies can effectively automate workflows through standardized integration of AI models/agents with CRM, ERP, project management platforms, etc.

Conclusion

Model context protocol (MCP) is a new interface developed by Anthropic to standardize the interaction between AI models/agents and third-party applications. Although MCP is still in its infancy and early stages of development, its widespread adoption could have a huge impact on the AI industry. A unified standard could eliminate the problems of siloed integration, allowing participants to build performant, robust, and scalable AI solutions faster and more efficiently.

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