Beyond local tools: Dive into the Model Context Protocol (MCP)
summary
MCP is shown as a JSON RPC protocol that standardizes tool, resource and prompt integration so an agent calls a single API instead of many REST/GraphQL endpoints. Live demo runs a Spring AI Java MCP server on :8080 with tools (add, multiply with an output schema), resources, elicitations for conditional prompts, and sampling where the server asks the agent to run text through its LLM. Transport options include stdio or streamable HTTP, auth uses OAuth dynamic client registration, and scaling uses sticky sessions, stateless mode, or per-user microVMs (firecracker) on AWS.
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chapters (20)
- 0:00Model Context Protocol intro
- 1:17MCP from LLM limits
- 4:09What MCP is and core parts
- 6:59MCP protocol structure
- 10:27MCP server with Spring AI
- 13:11Connect clients to servers
- 15:39Debugging with MCP Inspector
- 19:51Advanced tool features
- 23:22Notifications and progress
- 25:26Elicitations for input
- 28:36Sampling and reverse LLM
- 32:28URI data access
- 34:55Prompts for agent workflows
- 37:20MCP vs REST for agents
- 38:04Client feature support
- 40:10AWS in MCP ecosystem
- 40:55MCP transport options
- 44:28Auth in MCP servers
- 47:43Scaling MCP servers
- 49:51Summary and Q&A
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