Model Context Protocol (MCP) – Page 2
MCP Architecture: Components, Lifecycle, and Client-Server Tutorial
What Is Model Context Protocol Architecture? The model context protocol (MCP) architecture defines a structured way to extend the capabilities of large language models (LLMs) beyond their training data. It introduces a standardized communication layer that allows LLMs to interact with external tools, systems, and data sources. MCP architecture enables dynamic and distributed integration of […]
Defining and Implementing MCP Tools: a Practical Guide
What Are Model Context Protocol (MCP) Tools? This is part of a series of articles about Model Context Protocol. MCP tools are functions exposed by a Model Context Protocol (MCP) server that allow AI models (like Large Language Models) to interact with external systems, perform actions, and access data. These tools enable AI agents to […]
Building with MCP: Anthropic Guidance and Code Execution in Claude
What Is the Model Context Protocol (MCP) by Anthropic? This is part of a series of articles about the Model Context Protocol. The model context protocol (MCP) is a framework introduced by Anthropic for its language models, such as Claude. MCP improves dynamic tool use by enabling language models to interact with code execution environments […]
MCP Gateway: How It Works, Capabilities and Use Cases
What Is a Model Context Protocol Gateway? A Model Context Protocol (MCP) gateway is an intermediary layer that simplifies how AI applications connect to multiple MCP servers. It acts as a single point of entry for AI agents like Claude or ChatGPT to access external tools, resources, and workflows through the MCP protocol. Instead of […]
How MCP Servers Work, Use Cases and Notable Examples
What Is an MCP Server? This is part of a series of articles about Model Context Protocol. MCP servers are applications that expose AI agents to tools and services through the standardized Model Context Protocol (MCP), acting as a bridge between AI models and external data or functionality. They allow AI models to use tools […]
MCP Call Filtering: Stopping Prompt Injection and Securing Enterprise AI
As enterprises adopt Model Context Protocol (MCP) to connect AI agents and tools with internal systems, one of the biggest risks they face is untrusted or unsafe tool calls. Without safeguards, a malicious prompt, injected instruction, or poorly validated request could trigger dangerous behavior—such as exposing sensitive data, running unauthorized actions, or even spreading malware. […]