Model Context Protocol (MCP) is a solution that enables AI agents to interact uniformly with a variety of tools by creating appropriate interfaces (MCP servers). Ultimately, this allows such AI agents to run programs and operate on data in an up-to-date state. This article explores the fundamental architecture of MCP, comparing it with traditional APIs, and highlighting the key benefits of this standardized approach. We'll examine practical use cases across different domains, from software development to personal assistants, and provide insights into the current state of the MCP ecosystem. By understanding how MCP bridges the gap between language models and external applications, developers can leverage this protocol to create more powerful and contextually aware AI solutions with significantly reduced integration effort.

1. What is MCP, and why is a new protocol needed

Large Language Models have evolved into semi-autonomous systems capable of complex interactions with the real world. The main limitation of LLMs so far has been that they function in isolation from real-world systems and actual data - even OpenAI models only work with a limited set of programs or a video stream of screen streaming. By default, most LLMs do not have access to real-time information, cannot perform actions on external systems, and cannot execute code. To provide relevant context, users are forced to manually transfer information to and from the LLM interface. The Model Context Protocol (MCP) was developed to overcome these limitations.

Analogy: a secretary's instruction book. Imagine that you have a very smart secretary who can work with all kinds of tools, be it databases or complex programs. However, each tool uses its interaction format that the secretary doesn't understand. To solve this problem, there is an "instruction book" - the MCP protocol.

This book provides for each tool a standardized description of its functions, parameters, and expected responses in a single text format. This allows the secretary to understand how to use any tool without having to learn its specifics. Each tool has its own "MCP server" - a specialized "translator" that understands the language of that particular tool.

When you give a secretary a text instruction, he uses MCP to understand what tool is needed and how to formulate the request. He then sends the request to the appropriate MCP server, which converts it into a format that the tool understands. The tool executes the query and returns the response to the MCP server, which in turn converts it into a standardized text format that the secretary understands.

The secretary, in turn, formats the response into text that you can understand. In this way, MCPs and MCP servers provide a standardized and uniform way of communicating between the secretary (LLM) and the tools, allowing the secretary to use the various tools effectively without going into the details of their implementation.

MCP is an open standard initiated by Anthropic and being developed by the open-source community. The goal of this standard is to bring AI systems out of isolation by providing them with a standardized way to access relevant context and perform actions in other systems. MCP can be compared to a "USB port" for AI applications, providing a universal interface that allows any AI assistant to connect to different data sources or services without having to write individual code for each integration.

2. MCP Architecture

The MCP architecture is built on the principle of the client-server model. There are three main roles in this model: hosts, clients and servers.

• The host is the LLM-based application that initiates the connection. For example, Claude Desktop, an IDE, or any other tool with an embedded LLM. The host runs MCP clients on its side.
• The MCP client acts as an intermediary within the host application that maintains one-to-one connectivity with MCP servers. A separate MCP client instance is created for each data source or tool. The clients manage the flow of information, determine which resources should be made available to the LLM, and handle the execution of the tools. For example, if the LLM needs data from PostgreSQL, the MCP client formats the request to direct it to the appropriate MCP server.
• An MCP server is a lightweight program or service that provides data or functionality through an MCP interface. A server can encapsulate both local resources (file system or database) and remote services.

One of the key features of MCP is the standardized nature of the protocol, which ensures compatibility between different AI applications and external resources. This enables any LLM-based application to interact with external data sources in a uniform manner. This allows developers to create integrations once and deploy them across multiple AI platforms.

2.1 MCP client components

The building blocks of client and server are called primitives. On the client side, there are two primitives: Roots (security rules for accessing files) and Sampling (a request to the AI for help in performing a task, e.g., creating a database query). The key idea behind the MCP protocol is to simplify the client as much as possible and shift the main integration burden to the MCP server, which uses a uniform protocol.

2.2 MCP server components

MCP servers offer three basic primitives for LLMs to interact with the outside world: tools, resources, and hints.

Tools are functions that LLMs can call to perform specific actions. Examples include interacting with APIs (such as weather APIs), automating tasks, or performing calculations. Each tool has a name, description, and input schema. Typically, the LLM requests user permission before executing a tool.

Resources are structured data sources that LLMs can access and read. They are similar to GET endpoints in REST APIs, providing data without performing significant computation. Examples include file contents, database records, API responses, or real-time financial data. Resources are often identified using URI-like identifiers.

Prompts are pre-defined dialogue templates or scripts that standardize interactions and help LLMs use tools and resources. They provide consistent results, can take dynamic arguments and include context from resources.

2.3 Interaction flow: how MCP servers extend LLM capabilities

The interaction between an LLM application (host), an MCP client, and an MCP server typically occurs in the following sequence:

1. Capability Discovery: The MCP client asks the server for a description of its capabilities (lists of tools, resources, hints).
2. Augmented Prompting: User request and context are sent to the LLM along with capability descriptions, allowing the model to "know" what it can do via the server.
3. Tool/Resource Selection: The LLM analyzes the request and available capabilities, selecting the right tool or resource, responding in a structured manner according to the MCP specification.
4. Server Execution: The MCP client invokes an action on the server, which executes it (e.g. a database query) and returns the result.
5. Response Generation: The client sends the result back to the host, which uses it to respond to the user.

The capability discovery mechanism allows the LLM to dynamically understand available tools and resources, enabling more flexible and context-aware interactions. This eliminates the need to hard-code specific integrations in the LLM application itself.

3. MCP and traditional APIs: key differences

Many developers have a question: is MCP just a wrapper over API? Let's see what the difference is between these concepts.

MCP is a protocol, not an interface. MCP protocol can support various interaction interfaces.  Unlike the traditional REST protocol, MCP client-server interaction is fundamentally two-way. MCP uses JSON-RPC 2.0 message format. Communication can be carried out over various transport protocols, usually STDIO for local servers and HTTP using Server-Sent Events (SSE) for remote servers. WebSockets are also supported.

Unlike stateless REST APIs, AI agents need context to work effectively. REST APIs are designed for machine-to-machine communication, but are not optimized for AI agents.

Each service has its API, so for each data source, the AI agent developer has to create its solution to interact with these APIs. The main idea behind the MCP protocol is to replace the many different ways of integrating LLMs with APIs with a single standard. Any AI model or agent will be able to interact with external data as long as there is a corresponding MCP server for that source. That is, the MCP concept solves the so-called MxN problem - you only need to create N server solutions for each of the tools, otherwise, the number of integrations would be equal to multiplying the number of M models by the number of N tools. At the same time, often each company makes its implementation of such solutions.

Semantic Description. An API provides its features through a set of fixed endpoints with known behavior. To add new features to the API, API developers must create a new endpoint or modify an existing endpoint. Any client that requires a new capability must also make changes to accommodate the API changes. This problem has led to the need for API versioning. But in the case of the MCP, the interface itself is both documentation, since the MCP sends the current interaction mechanism upon initial interaction.

Also, if necessary, the MCP client can perform sampling, which allows servers to utilize the artificial intelligence capabilities on the MCP client side.

APIs are deterministic, while MCP calls are not. This means that, in general, models can adapt to call the right tool, but errors are possible. This is similar to how probabilistic ML models for detecting objects in images turn out to be better than deterministic, manually created rules, but still require both resources and can make mistakes. However, in the case of MCPs, error reporting allows the agent to achieve the result with LLM support.

MCPs are initially aimed more at non-deterministic behavior, as the protocol is specifically designed to interact with tools that are not normally available from APIs: running terminal commands, working with databases, and so on.

4. Advantages of using MCP servers in LLM applications

Using MCP servers for LLM integration provides a number of key benefits:

• Standardized AI integration: MCP provides a structured and consistent way to connect AI models to tools and data.
• Reduced development effort: MCP simplifies integration by reducing the need to custom code for each new tool, data source, and each new model. Developers create an integration once and can use it across multiple AI platforms. When all tools speak the same language (JSON-RPC 2.0) it's easier to replace MS Teams with Slack or Jira with Linear or use Claude models instead of OpenAI models.
• The ability to create a single entry point: when using MCP, it is actually sufficient to have a single entry point that operates the tools. Since the LLM has a common context, the need for user switching between the contexts of different applications is eliminated. In fact, it is possible to operate all MCP-enabled applications from a single chat room.
• Improved security: offers better control over data access and tool execution through user consent and defined permissions. Servers isolate credentials and sensitive data. In addition, you can host MCP server data directly on your local hardware. This can make it easier for companies to comply with GDPR regulations.
• Real-time data access: allows LLMs to access up-to-date information, resulting in more accurate and contextually relevant responses.
• Modularity and reuse: Enables the creation of reusable and modular connectors for different platforms. Tools and resources can be independently updated, tested and reused.
• Flexibility: Allows you to easily switch between different AI models and vendors.
• Workflow Automation: Facilitates the creation of complex and automated workflows by connecting the LLM to various tools and services. For example, you can provide a simple database migration from one view to another.
• Reduced network and server load: MCP optimizes data exchange by transmitting only the necessary information, which reduces network and server load compared to traditional solutions.
• Scalability: The MCP architecture provides high scalability, making it easy to add new tools and resources without changing the underlying system.
• Multilingual and cross-platform: MCP is independent of programming language or platform, which ensures broad compatibility and flexibility when integrating different systems.

5. Practical examples of using MCP

Software development

An AI assistant connected via MCP to GitHub, IDEs and project management systems can help developers automate routine tasks. For example, on the request "Fix a bug in the authentication module", the assistant can analyze the code from the repository, find the problem, suggest a fix, and automatically create a pull request.

Data analysis and business intelligence

When connected via MCP to databases, visualization tools, and business systems, the AI assistant can handle complex analytical queries. A user can ask a question like "How did sales by region change over the last quarter?" and the assistant will independently extract relevant data, analyze it, and present the results in a visualized form.

Workflow automation

MCP allows you to create automated scenarios that combine multiple tools. For example, when a new customer fills out a form on the website, an AI assistant can automatically create a contact in CRM, send a welcome email, add a task for a manager in the project management system, and notify the team in corporate messenger.

Personal assistants

MCP makes it possible to create truly effective personal assistants capable of working with calendar, e-mail, documents and other user tools. The request "Prepare a report of my meetings from last week and send it to the participants" can be fully automated.

6. State of the MCP ecosystem today

Technology maturity. MCP servers have already been written for Slack, Gmail, Google Drive, sites with OpenAPI, Figma, GitHub, Blender, Ableton, Perplexity Sonar, Zapier, QGIS, Firecrawl, PubMed publications, Gradio, Stripe, Unity.

There are also already directories where users can find and install MCP servers. For example, MCP.so, Glama, or Cline's MCP Marketplace. Tools for working with MCP groups are also emerging, such as the mcpz utility.

The developer community is actively creating frameworks for rapid deployment of MCP servers, which significantly lowers the barrier to entry for integrating new tools.

Conclusion

The MCP concept is a promising approach to address the growing challenges of managing large language models.

As the MCP ecosystem expands and the number of supported tools increases, we can expect significant progress in the practical application of artificial intelligence in everyday tasks. The open nature of the protocol and the active participation of the developer community ensure continuous improvement of MCP and the emergence of new integrations.

Sources

• GitHub: https://github.com/modelcontextprotocol
• Introduction to MCP official documentation architecture and capabilities
• Introduction to Model Context Protocol from Anthropic description of the standard