Model Context Protocol
The open standard to connect AI with tools and data
What is the Model Context Protocol?
The Model Context Protocol (MCP) is an open standard that enables AI systems to communicate with external tools and resources in a structured, secure manner. It provides a bridge between large language models and the broader digital ecosystem, allowing models to access real-time information, perform computations, and interact with external systems.
MCP was introduced by Anthropic in November 2024. You can read more about the initial announcement in their official press release, which outlined the vision for creating a more capable and extensible AI ecosystem.
Why MCP Matters
Large language models are trained on vast amounts of data, but they have inherent limitations. They can't access real-time information, perform complex calculations reliably, or interact with external systems. MCP addresses these limitations by providing a standardized way for models to request and receive information from specialized servers.
With MCP, AI models can:
- Access up-to-date information beyond their training data
- Perform complex calculations with high accuracy
- Interact with external APIs and services
- Retrieve specialized knowledge from domain-specific tools
- Execute actions in the real world through connected systems
How MCP Works
At its core, MCP defines a communication protocol between AI models and MCP servers. These servers provide tools and resources that extend the model's capabilities:
- Tools: Functions that perform specific operations, such as calculations, API calls, or data transformations
- Resources: Data sources that provide information, such as databases, documents, or real-time feeds
When an AI model needs information or functionality beyond its capabilities, it can request assistance from an MCP server. The server processes the request and returns the results, which the model can then incorporate into its response.
Benefits of MCP
MCP offers significant benefits for AI developers and users:
- Enhanced Capabilities: Models can perform tasks that would otherwise be impossible
- Improved Accuracy: Access to specialized tools reduces errors in complex domains
- Real-time Information: Models can work with current data rather than outdated training information
- Extensibility: Developers can create custom servers for specific domains or applications
- Standardization: A common protocol simplifies integration across different models and systems
Getting Started with MCP
Whether you're looking to use existing MCP servers or develop your own, our resources provide everything you need to get started. From comprehensive documentation to step-by-step tutorials, you'll find guidance for every stage of your MCP journey.