Prisma AIRS
Prisma AIRS MCP Server for Centralized AI Agent Security
Table of Contents
Prisma AIRS MCP Server for Centralized AI Agent Security
Model Context Protocol is a standardized communication framework that connects Large
Language Models (LLMs) with external tools, data sources, and services through a unified
interface.
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Model Context Protocol (MCP) is a standardized
communication framework that acts as a medium between LLMs and contextual information
like tools and prompts.
MCP acts as a standardized communication layer between LLMs and tools,
requiring all tool providers to follow the same protocol. This enables organizations to
build AI Agents that can easily integrate with various tools through a unified
interface, simplifying development and ensuring consistent interactions.
MCP streamlines AI development by replacing custom tool integrations with a
standardized communication protocol. This approach reduces maintenance overhead,
simplifies updates, and provides a scalable architecture that can efficiently connect
LLMs to hundreds of external tools through a single, unified interface.
The Model Context Protocol architecture consists of three interconnected
components that work together to enable seamless AI-tool integration.
- MCP Host represents the AI Agent or environment where AI-driven tasks are performed, such as Claude Desktop or Cursor, an AI-driven development tool. This host operates the MCP client and serves as the primary interface for integrating tools and data while enabling interaction with external services.
- MCP Client acts as a crucial intermediary that facilitates all communication between the MCP host and various MCP servers. The client is responsible for sending requests and gathering comprehensive information about available server services, ensuring smooth data flow throughout the system.
- MCP Server functions as a gateway that enables client interaction with external services. Each server executes tasks through three essential functionalities: Tools that invoke external services and APIs to execute tasks on behalf of the AI model, Resources that expose both structured and unstructured datasets from sources like local files, databases, and cloud platforms, and Prompts that manage reusable templates to enhance model responses, maintain consistency, and simplify repeated actions.
