09-26, 10:05–10:55 (Europe/Amsterdam), Voyager
Large‑language‑model agents are only as useful as the context and tools they can reach.
Anthropic’s Model Context Protocol (MCP) proposes a universal, bidirectional interface that turns every external system—SQL databases, Slack, Git, web browsers, even your local file‑system—into first‑class “context providers.”
In just 30 minutes we’ll step from high‑level buzzwords to hands‑on engineering details:
- How MCP’s JSON‑RPC message format, streaming channels, and version‑negotiation work under the hood.
- Why per‑tool sandboxing via isolated client processes hardens security (and what happens when an LLM tries
rm ‑rf /
). - Techniques for hierarchical context retrieval that stretch a model’s effective window beyond token limits.
- Real‑world patterns for accessing multiple tools—Postgres, Slack, GitHub—and plugging MCP into GenAI applications.
Expect code snippets and lessons from early adoption.
You’ll leave ready to wire your own services into any MCP‑aware model and level‑up your GenAI applications—without the N×M integration nightmare.
Large‑language‑model agents are only as useful as the context and tools they can reach.
Anthropic’s Model Context Protocol (MCP) proposes a universal, bidirectional interface that turns every external system—SQL databases, Slack, Git, web browsers, even your local file‑system—into first‑class “context providers.”
In just 30 minutes we’ll step from high‑level buzzwords to hands‑on engineering details:
- How MCP’s JSON‑RPC message format, streaming channels, and version‑negotiation work under the hood.
- Why per‑tool sandboxing via isolated client processes hardens security (and what happens when an LLM tries
rm ‑rf /
). - Techniques for hierarchical context retrieval that stretch a model’s effective window beyond token limits.
- Real‑world patterns for accessing multiple tools—Postgres, Slack, GitHub—and plugging MCP into GenAI applications.
Expect code snippets and lessons from early adoption.
You’ll leave ready to wire your own services into any MCP‑aware model and level‑up your GenAI applications—without the N×M integration nightmare.
Schedule
0 – 5 mins: The integration headache — why function‑calling alone can’t scale.
5 – 15 mins: Under the Hood — MCP architecture, JSON‑RPC anatomy, bidirectional streaming & security sandboxing.
15 – 25 mins: Orchestrating Tools — patterns and multi‑tool workflows, with code examples.
25 – 30 mins: Ecosystem & Q/A — pre‑built servers, SDKs, when MCP is overkill.