Context is the new code. Master the discipline of engineering what AI knows, when it knows it, and how it acts — using MCP as the protocol backbone. Three concept levels — Basics, Hands-on & Advanced — from foundational theory to production-grade agentic systems.
Prompt engineering tells the AI what to do. Context Engineering controls what the AI knows — the tools it can call, the data it can read, the memory it carries. MCP is the open protocol that makes context delivery programmable, scalable, and secure.
Functions an AI model can call — like searching the web, running code, querying a database, or sending an email. Think of them as the "hands" of your AI agent.
Structured data an AI can read — files, database records, API responses, live metrics. Resources are read-only, giving the AI "eyes" into your world.
Pre-built, reusable prompt templates that guide AI behavior. Teams share consistent, versioned instructions — eliminating prompt drift across your organization.
The evolution from prompting to context architecture — and why MCP is the protocol that makes it real at scale.
When context is engineered — not improvised — a new class of reliable, production-grade AI applications becomes possible.
AI agents that read your codebase, run tests, create PRs, and deploy — all through MCP servers connected to GitHub, CI/CD pipelines, and cloud providers.
Connect Claude directly to your databases, analytics platforms, and data warehouses. Ask natural language questions and get SQL-backed answers instantly.
MCP servers that index Notion, Confluence, Slack, and Google Drive — giving your AI a unified semantic search across all company knowledge.
AI copilots that can query Kubernetes clusters, read CloudWatch logs, scale services, and respond to incidents — all through secure MCP tool calls.
Agents that manage inventory, process orders, handle customer queries, and update product listings across platforms — orchestrated via MCP.
Educational AI that tracks student progress, adapts content difficulty, fetches relevant resources, and generates custom exercises through connected MCP servers.
Zero to hero — structured by concept depth, not calendar days. Each level unlocks the next, building compounding understanding from protocol fundamentals to enterprise-grade agentic systems.
Every concept explained with real-world analogies first, then grounded in the MCP specification and Context Engineering principles. Organised by learning level.
The discipline of designing what information an AI model receives — not just what you ask it. Context Engineering controls tools, memory, instructions, and live data so models perform reliably, not just impressively in demos.
The Model Context Protocol is the open standard that makes context delivery programmable. Three primitives — Tools (actions), Resources (data), Prompts (instructions) — give AI a structured, permission-scoped view of the world.
Host → Client → Server. The Host is your app (Claude Desktop, VS Code), the Client manages connections, the Server exposes capabilities. Like a restaurant: host seats you, waiter routes orders, kitchen executes.
Models have finite token budgets. Context Engineering decides what earns a spot in that window — injected docs, retrieved chunks, tool results, conversation history. Overflow = hallucination and dropped context.
Tools are callable functions the AI invokes when it needs to act. JSON Schema defines inputs; your server handles execution. The AI decides when and why to call — your code decides what happens next.
The simplest MCP transport — server runs as a child process communicating via stdin/stdout. No networking, no auth layer, just clean pipes. The fastest path from zero to a running MCP server.
MCP Resources expose dynamic data — files, database records, API responses — via URI addressing. Instead of stuffing docs into prompts, the AI fetches exactly what it needs, when it needs it.
Secure context delivery requires scoped access. MCP's OAuth model ensures the AI only sees data it's authorized to access — per user, per tool, per data source. Context security = trust.
Resource subscriptions push live data changes to the AI without polling. Your agent responds to events as they happen — not to stale snapshots injected at conversation start.
Combining vector search with MCP Resource servers: semantic retrieval pipelines that surface the right knowledge chunks into context at the right moment — grounded, permission-scoped, and auditable.
Least-privilege context design: only give AI what it needs for the task. Input sanitization, rate limiting, and threat modeling prevent prompt injection and data leakage through the context layer.
Distributing context across specialized agents — each with purpose-built MCP servers. Tool namespacing, capability negotiation, and shared memory registries enable coherent multi-agent workflows.
Next-generation MCP transport: bidirectional streaming, connection resumability, and sub-100ms latency for high-frequency context updates. The foundation for real-time agentic systems.
AI calls tools, observes results, updates working context, and calls again — across hundreds of steps. Designing MCP servers for autonomous operation with human-in-the-loop approval gates.
Distributed tracing of every context injection and tool call. Token budget monitoring, latency histograms, and cost attribution — because you can't optimize what you can't measure.
MCP registry design, server versioning, blue-green deployments, and organizational governance for managing 50+ MCP context servers across business units at enterprise scale.
No theory without a terminal. Every concept maps to a real project you build, test, and ship.
Bookmark these. The exact patterns and API signatures you reach for every time you build an MCP server.