The Model Context Protocol is an open standard for connecting LLM apps to external tools and data. Rather than hard-coding a fresh integration in every app, you build an MCP server once that exposes tools and resources, and any MCP-aware client — Claude, or your own agent — can use them. It turns agent capabilities into modular, reusable building blocks.
Curriculum
For 1:1, built around tools your agents actually need
1 · Why MCP
The problem MCP solves, how it compares to bespoke tool calling, and where it fits in an agent architecture.
2 · Protocol basics
Servers, clients, tools, resources, and prompts — the core primitives and how they fit together.
3 · Build a server
Stand up a working MCP server, expose your first tools and resources, and test it.
4 · Tool design
Designing tools an agent can use reliably — clear schemas, good errors, and safe side effects.
5 · Transports & auth
stdio vs HTTP transports, deployment options, and handling authentication and secrets.
6 · Connect to agents
Wiring MCP into a LangGraph agent and into clients like Claude, and reasoning about tool selection.
7 · Safety
Permissions, guardrails, and keeping a tool-using agent from doing something you didn't intend.
8 · Ship & maintain
Packaging, versioning, and evolving MCP servers as your tool surface grows.
Formats & Details
1:1 Private Mentoring
Built around the tools and data you need to expose.
- 4–10 hours across sessions
- Ship a real MCP server
- Live review + async follow-up
Team Workshop
Get a platform team fluent in MCP fast.
- 1–2 days, live online
- Labs + your integrations
- Reusable server template
Prerequisites
- Comfortable with Python or TypeScript
- Basic API experience
- No prior MCP needed
Frequently Asked Questions
What is the Model Context Protocol?
MCP is an open standard for connecting LLM apps to external tools and data. You build a server that exposes tools and resources, and any MCP-aware client (like Claude or your own agent) can use them — making capabilities modular and reusable.
Who is it for?
Engineers building agents or internal AI platforms who want a clean, reusable, safe way to give models access to tools, APIs, and data.
Do we build a real MCP server?
Yes — you build one, expose tools and resources, and connect it to an agent or client, leaving with running code.
How much does it cost?
Depends on format and duration — book a free intro call for a tailored proposal.
Related Training
Agentic AI Training →
Use MCP tools inside a full agent stack.
LangGraph Training →
Wire MCP tools into a stateful graph.
RAG Training →
Expose retrieval as an MCP tool.
AI Architecture Mentoring →
Design a tool platform, not just one server.