In many LLM applications today, connecting to tools or external data sources still means writing custom logic for each integration. As more teams build with AI, this approach is slow and fragmented.
The Model Context Protocol (MCP), developed by Anthropic, addresses this problem by introducing a shared standard. MCP defines how LLM applications can access tools, data, and prompts using a client-server architecture, making integrations easier to build, reuse, and maintain.
To help you understand and apply this emerging protocol, we’ve created the course “MCP: Build Rich-Context AI Apps with Anthropic,” taught by Elie Schoppik.
You’ll learn how MCP works under the hood, how to build your own server, and how to connect it to Claude-powered applications locally or remotely.
DeepLearningAI
🚨 New course on MCP with Anthropic!
In many LLM applications today, connecting to tools or external data sources still means writing custom logic for each integration. As more teams build with AI, this approach is slow and fragmented.
The Model Context Protocol (MCP), developed by Anthropic, addresses this problem by introducing a shared standard. MCP defines how LLM applications can access tools, data, and prompts using a client-server architecture, making integrations easier to build, reuse, and maintain.
To help you understand and apply this emerging protocol, we’ve created the course “MCP: Build Rich-Context AI Apps with Anthropic,” taught by Elie Schoppik.
You’ll learn how MCP works under the hood, how to build your own server, and how to connect it to Claude-powered applications locally or remotely.
🔗 Enroll now! hubs.la/Q03mtYxW0
7 months ago | [YT] | 37