Give your AI agent live web data via MCP
Ask an assistant what a product costs today and you will usually get a number. It is often wrong, and it is always wrong in the same way: the model is reconstructing a plausible price from training data rather than looking at a page. This course is about closing that gap properly — what the Model Context Protocol actually is, how to wire a server into a client, how to design tools a model can use without hand-holding, and what to put in place before an agent spends your money.
What you will be able to do
- Explain MCP to a colleague in two sentences without using the word "ecosystem"
- Tell the difference between a model that does not know something and a model that has been given a tool badly
- Connect an MCP server to a client and verify that the tools are actually registered
- Write a tool description a model picks correctly on the first attempt
- Give an agent the ability to fetch a real price from a real page
- Put a spend cap, a rate limit and an injection boundary in place before any of this touches production
The six lessons
Lessons one and two are concepts and cost nothing to read. From three onwards you will want a client installed.
- 01What MCP actually is, in plain termsThe Model Context Protocol described without jargon: what problem it solves, its three primitives, and when it is the wrong tool.9 min read Read lesson 1 →
- 02Why your agent's answer about a price is wrongFour distinct failure modes that all look identical from the outside, and how to tell which one you have before you try to fix it.10 min read Read lesson 2 →
- 03Connecting an MCP server and proving it worksThe config for local and remote servers, the four things that go wrong, and how to verify the tools registered rather than assuming.10 min read Read lesson 3 →
- 04Designing tools an agent can actually useA connected server is not a useful server. The model only sees your tool names, descriptions and parameter schemas, so those three things are the entire user interface. Here is what makes a tool get called correctly and what makes it get ignored.11 min read Read lesson 4 →
- 05Giving an agent a real price feedNeeds an accountA worked example. Connect the ScrapeWise MCP server to a client, let the agent read a live scraper's output, and watch where the hand-off between "the data is right" and "the answer is right" actually breaks.12 min read Read lesson 5 →
- 06Guardrails, cost control and untrusted contentLive web access turns an agent into something that can spend money and read text written by strangers. Neither is a reason not to do it. Both are reasons to put limits in before you need them.11 min read Read lesson 6 →
Who this is for
Written for
- Developers building on Claude, ChatGPT or an agent framework who need the agent to see the live web
- Technical founders evaluating whether MCP is worth adopting
- Data teams who already have an API and are deciding whether to expose it to agents
Not written for
- Anyone looking for a no-code agent builder — this assumes a config file and a terminal
- Readers who want the full specification; this is the working subset, and the spec is linked where it matters
Before you start
The questions that come up in the first ten minutes.
No. Lesson one assumes nothing beyond having used an AI assistant. If you already know what a tool call is, skim it and start at lesson two.
Start at lesson one
The Model Context Protocol described without jargon: what problem it solves, its three primitives, and when it is the wrong tool.