MCP: Build Rich-Context AI Apps with Anthropic explores the Model Context Protocol (MCP) and how it can be used to connect Large Language Models and AI applications to external tools, data, and contextual resources. Developed in collaboration between DeepLearning.AI and Anthropic and taught by Elie Schoppik, the course introduces MCP as an open protocol for creating standardized integrations between AI applications and external systems. 

A central challenge when developing LLM applications is providing models with access to information and capabilities outside their training data and immediate prompt context. Traditionally, these integrations have required custom implementations for individual applications, APIs, databases, and data sources. MCP addresses this problem by defining a common interface through which AI applications can discover and access tools, resources, and prompt templates provided by external systems.

The course introduces the client-server architecture of MCP. An AI application hosts one or more MCP clients, while MCP servers expose capabilities that the application can access. Servers can operate locally as subprocesses or remotely as independent services. This separation provides a reusable architecture where the same MCP server can potentially provide capabilities to different AI applications without requiring application-specific integrations.

An important distinction is made between MCP’s three principal capabilities. Tools allow an AI application to perform actions by invoking functions; Resources provide contextual information and data; and Prompts provide reusable prompt templates and interaction patterns. Together, these mechanisms enable an LLM application to dynamically combine model reasoning with external information and software functionality.

The course progresses from understanding MCP architecture to implementing complete MCP-enabled applications. It demonstrates how an existing chatbot can be transformed into an MCP-compatible application, how MCP servers and clients are created, how multiple servers can be connected to the same application, and how servers can be deployed remotely. The course also demonstrates integration with existing MCP reference servers and applications such as Claude Desktop. 

Overall, the course provides a practical understanding of MCP as an integration layer for modern AI systems. Rather than embedding every external capability directly into an application, MCP introduces a standardized architecture where models, applications, tools, and information sources can be connected as reusable components. This provides an important foundation for building richer LLM applications and increasingly capable agentic AI systems.

Topics and techniques

• Model Context Protocol (MCP) – standardized integration between AI applications and external systems.
• MCP architecture – understanding hosts, clients, servers, and their communication.
• Tools, Resources, and Prompts – exposing actions, contextual data, and reusable prompt templates.
• MCP Servers – building local and remote servers with FastMCP.
• MCP Clients – enabling AI applications to discover and interact with MCP servers.
• MCP Inspector – testing and debugging MCP servers.
• Reference Servers – connecting applications to reusable filesystem and web-content services.
• Claude Desktop integration – configuring an existing AI application to use MCP servers.
• Remote MCP Servers – deploying MCP capabilities beyond the local environment. 

Practical exercises

The practical exercises use Python to progressively transform a conventional LLM chatbot into an MCP-compatible AI application. The initial chatbot implements custom tools for searching and retrieving academic papers. These capabilities are then moved into an MCP server, exposing them through a standardized interface rather than application-specific tool implementations.

The exercises continue by building an MCP client that dynamically connects the chatbot to the server and by integrating additional reference servers for filesystem operations and retrieving web content. Further exercises add MCP Resources and Prompts, configure the developed server for use with Claude Desktop, and finally deploy an MCP server remotely. Together, the exercises demonstrate the complete path from a custom LLM integration to a modular architecture in which AI applications can access multiple independent MCP servers and their tools, resources, and prompts

erence servers for filesystem operations and retrieving web content. Further exercises add MCP Resources and Prompts, configure the developed server for use with Claude Desktop, and finally deploy an MCP server remotely. Together, the exercises demonstrate the complete path from a custom LLM integration to a modular architecture in which AI applications can access multiple independent MCP servers and their tools, resources, and prompts.