Model Context Protocol

What is Model Context Protocol?

The Model Context Protocol (MCP) is a standardized way for AI models to securely connect with external data, tools, and services, similar to how a USB-C port provides a universal connection for many devices. Developed by Anthropic, MCP allows AI applications to request and receive information or execute actions through "servers" that expose "resources" (data), "tools" (functions), and "prompts" (templates), enabling AI to access real-time information and perform tasks beyond its initial training data.

How does it work?

1. AI Request: An AI model needs information or to perform an action, such as checking inventory levels or fetching current weather data.
2. MCP Server: An MCP server, hosted by the service provider (e.g., a database company), exposes its capabilities to the AI.
3. Standardized Communication: The MCP protocol provides a standard format for the AI to request data (resources) or initiate actions (tools).
4. Information Retrieval/Action: The server processes the request, retrieving data from a database or using a tool to perform an action.
5. Contextual Integration: The server returns the requested information to the AI, which integrates it into its understanding of the conversation or task.
6. Informed Response: The AI can then provide a more accurate, up-to-date, and relevant response based on the real-world data it received.

What are the key components?

• Resources: Data retrieved from sources like databases or external systems.
• Tools: Functions that an AI can call to perform actions, like making an API request or running a calculation.
• Prompts: Reusable templates for the AI to interact with servers and manage workflows.

Why is it important?

• Universal Connectivity: MCP acts as a "universal adapter" for AI, allowing it to easily connect to any compatible tool or data source without custom integrations.
• Real-time Data & Actions: It enables AI to access current, real-world information, preventing it from hallucinating or relying on outdated training data.
• Security: MCP provides a secure way for AI to interact with sensitive data and systems through explicit permissions and standardized authorization, making it suitable for enterprise use.
• Scalability: By standardizing connections, MCP makes it easier to build and scale AI agent systems that can interact with a vast network of external tools and data sources.