dictionary · MCP & Tool Integration
What is MCP? A simple guide to the standard protocol connecting AI with external tools
A beginner-friendly explanation of MCP, how AI connects to external tools and data, and how Hosts, Clients, and Servers work together.
Definition
MCP (Model Context Protocol) is an open protocol for connecting AI applications with external tools and data through a standardized interface.
If you want an AI system to read company files, search a database, or operate an external service, the AI model alone does not automatically have direct access to those environments. MCP provides a common connection layer between the AI application and external systems so that available capabilities and data can be exposed in a consistent form.
The official MCP specification defines MCP as a protocol for integrating AI applications with external data sources and tools.
How it works
A useful way to understand MCP is to separate the system into three parts: Host, Client, and Server.
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Host: the application in which the user interacts with AI
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Client: the component inside the Host that manages the connection to an MCP Server
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Server: the component that exposes tools, data, prompts, and other capabilities to the AI application
For example, if an AI application needs file search, an MCP Server can expose a tool such as search_files, and the AI application can call that tool through its MCP Client.
An MCP Server can commonly provide three kinds of capabilities.
Tools
Functions that an AI model can invoke.
Examples:
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Search files
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Query a database
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Call an external API
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Modify an allowed file
Resources
Data or context that AI systems or users can access.
Examples:
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File contents
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Documents
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Git history
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Database records
Prompts
Reusable instructions or workflows that users can invoke.
Because these capabilities can be represented through a common protocol, developers can reduce the need to build a completely different integration method for every AI application.
In the MCP 2026-07-28 specification, the base protocol uses JSON-RPC 2.0 and has evolved toward stateless, self-contained requests. That is mostly an implementation detail. For most users, it is enough to think of MCP as a common standard for connecting AI to external capabilities.
Commonly confused concepts
MCP vs. Tool Calling
Tool Calling is the mechanism that lets an AI model decide when and how to invoke a tool.
MCP standardizes how an AI application connects to and discovers externally provided tools and data.
In short:
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Tool Calling = how AI uses a tool
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MCP = the common connection standard between the AI application and the tool provider
A common architecture is for an MCP Server to expose a tool and for the AI model to invoke that tool through Tool Calling.
MCP vs. APIs
An API is an interface defined by an individual service.
MCP does not replace APIs. An MCP Server can call an existing API internally.
One useful way to think about MCP is as a common interface layer that makes service-specific APIs easier for AI applications to use consistently.
MCP vs. Computer Use
Computer Use lets AI interact with graphical interfaces in a way that resembles a human operating a screen.
MCP instead uses predefined Tools and Resources in a structured form.
That means the two approaches can serve different roles:
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Structured operations that can be exposed clearly as APIs or Tools → MCP
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Tasks that are only available through a graphical interface → Computer Use
How this relates to OGINAU
OGINAU uses MCP in real product integrations.
For example, OGINAU Remote does not give AI unrestricted access to a user’s Windows PC. Instead, an MCP Server exposes only approved tools, and those tool calls travel through an authenticated and permission-controlled path to the Windows Connector.
Conceptually, the architecture looks like this:
AI application
↓
MCP Client
↓
OGINAU Remote MCP Server
↓
Authentication and permission control
↓
Windows Connector
↓
User-owned PCDuring OGINAU Remote development, we built and tested end-to-end paths for listing files, reading files, writing files, creating folders, and other bounded operations through MCP.
That experience shows that the value of MCP is not limited to simply adding tools to an AI system.
A major benefit is that the AI application and the external execution environment can share a common tool contract while still placing authentication and permission controls between them.
MCP does not automatically make a system safe. The official specification also emphasizes that tool execution and data access require user consent, appropriate access controls, and privacy protections.
Sources and primary information
This article is based on the following primary sources and implementation records.
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Model Context Protocol Specification 2026-07-28: official MCP specification covering the protocol definition, Host / Client / Server architecture, Tools / Resources / Prompts, JSON-RPC, and security principles.
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MCP TypeScript SDK v2 Documentation: official SDK implementing the 2026-07-28 specification and the current model for exposing Tools, Resources, and Prompts.
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OGINAU Remote development records: internal primary records covering an implementation that combines an MCP Server, tool exposure, OAuth authentication, a Windows Connector, and end-to-end Windows PC tests.
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OGINAU Browser development records: internal primary records covering a Browser Tool path using MCP Client → Gateway → Browser Bridge → Chromium Extension.
Official references:
Updated information: This article was prepared using the MCP specification available as of October 4, 2026.
Summary
MCP is an open protocol for connecting AI applications with external tools and data through a standardized interface.
The key point is that MCP is not itself an AI model. It is a shared set of connection rules between AI applications and external systems.
Understanding MCP makes it easier to see how AI can use files, business systems, browsers, computers, APIs, and other external capabilities as practical tools for real work.