What is the Model Context Protocol? - Explanation & Meaning

Definition

The Model Context Protocol (MCP) is an open standard, developed by Anthropic, that defines a universal interface through which AI models and agents can securely connect to external data sources, tools, and services.

Technical explanation

MCP standardizes how AI agents communicate with the outside world via a client-server architecture. An MCP server exposes capabilities — tools (functions that perform actions), resources (data sources that can be read), and prompts (reusable instructions) — via a standardized JSON-RPC protocol. MCP clients (such as AI agents or IDEs) discover available capabilities through capability negotiation and invoke them via a type-safe interface. The protocol supports multiple transport layers: stdio for local communication, HTTP with Server-Sent Events for remote connections, and WebSocket for bidirectional real-time communication. In 2026, MCP has grown into the de facto standard for AI tool use, comparable to how HTTP became the standard for the web. Major platform vendors including OpenAI, Google, and Microsoft have integrated MCP support. MCP servers exist for databases (PostgreSQL, MySQL), cloud services (AWS, GCP), development tools (GitHub, Jira), CRM systems (Salesforce, HubSpot), and many other services. The protocol includes built-in security layers: permission models, authentication via OAuth 2.0, and audit logging of all tool invocations.

How MG Software applies this

MG Software uses MCP as the standard protocol for the AI agents we build. We develop custom MCP servers that expose business-specific tools and data sources, enabling AI agents to securely interact with our clients' CRM systems, databases, and internal applications. MCP allows us to build agents that are modular and reusable.

Practical examples

• A company building an MCP server for their CRM system, enabling AI agents to look up customer information, review interaction history, and create follow-up tasks — all via a standardized, secured interface.
• A development team using an AI assistant in their IDE that connects via MCP to their GitHub repository, Jira board, and documentation site, enabling the assistant to read code, create issues, and update documentation.
• A data analytics platform implementing MCP servers for their databases and BI tools, allowing an AI agent to perform ad-hoc analyses, generate charts, and compile reports based on natural language questions.

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