MCP, from the architecture up: clients, servers and what they expose
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New course: MCP: Build Rich-Context AI Apps with Anthropic. Learn to build AI apps that access tools, data, and prompts using the Model Context Protocol in this short course, created in partnership with Anthropic @AnthropicAI and taught by Elie Schoppik @eschoppik, its Head of Technical Education. Connecting AI applications to external systems that bring rich context to LLM-based applications has often meant writing custom integrations for each use case. MCP is an open protocol that standardizes how LLMs access tools, data, and prompts from external sources, and simplifies how you provide context to your LLM-based applications. For example, you can provide context via third-party tools that let your LLM make API calls to search the web, access data from local docs, retrieve code from a GitHub repo, and so on. MCP, developed by Anthropic, is based on a client-server architecture that defines the communication details between an MCP client, hosted inside the AI application, and an MCP server that exposes tools, resources, and prompt templates. The server can be a subprocess launched by the client that runs locally or an independent process running remotely. In this hands-on…
Context
The gives AI applications a common interface for reaching external capabilities. In the course announcement, Anthropic's protocol connects a client inside an AI application to a server that exposes tools, data resources, and reusable prompt templates.
A server can run locally or remotely. In the course's academic-paper chatbot exercise, a chatbot with paper-search tools is converted into an MCP-compatible application and connected to a server it builds, then to other servers. The shared interface is meant to reduce the custom integration work otherwise needed for each application and external system.
- MCP — The Model Context Protocol — an open standard that lets any AI assistant plug into any tool or data source without custom integration code.
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