MCP from the Ground Up: Connect AI to Data and Tools with Vibe Coding
A workshop in which you will build MCP servers in Python that expose tools, documents, and API data to an AI model. You will learn how to design contracts for tools, resources, and prompts, delegate code to an AI agent, test it in MCP Inspector, diagnose errors, and secure read and write operations. The course ends with your own server handling a business process.
Access to the full course
45 USD−17%
38 USD
- ✓One-time payment
- ✓Lifetime access
- ✓Certificate
- ✓12 hours
Who this course is for
Developers, integration architects, technical analysts, and people building AI automations who want to safely connect models to data and operations. The course is for MCP beginners, including those who do not write Python fluently but can run a project and read simple code. The material will be especially useful for people using programming agents who want to move from generating random code snippets to a controlled process: specification, implementation, test, diagnosis, and fix.
The course takes you from understanding the problem of integrating AI models with systems to a working MCP server. Instead of rewriting long blocks of code, you work like a technical lead using a programming agent: you describe the contract, assign a small increment, run the result, check the behavior, and come back with a specific error. Using a shared customer support assistant example, you will connect the model with an external API, local documents, and a simple case register. You will see full examples of weak and improved prompts, failed and correct tool responses, as well as tests that reveal errors not visible on the happy path. The implementation uses Python, the official MCP SDK with FastMCP, and MCP Inspector. The focus is on stable contracts and patterns, not on tying yourself to an experimental version: before starting the project, the participant verifies the current stable SDK version and pins dependencies. The finale is your own MCP server implementing a simple business process with validation, access control, write-operation confirmation, and a set of acceptance tests.
What you will learn
- You will explain what problem MCP solves and where the boundary of responsibility lies between the model, the MCP client, and the server.
- You will distinguish an MCP server from a regular API and function calling, and choose the right mechanism for a specific integration.
- You will design tools, resources, and prompts so that they have unambiguous names, input schemas, predictable outputs, and clear errors.
- You will prepare an MCP server project in Python, pin dependencies, and run it via stdio or Streamable HTTP transport.
- You will assign an AI agent small, verifiable changes instead of generating the entire application in one prompt.
- You will build an MCP tool that fetches data from an external API, with timeout, validation, and controlled error mapping.
- You will expose local documents as safely restricted resources and add a tool for searching them.
- You will test listing and invoking server capabilities in MCP Inspector and record repeatable test cases.
- You will connect a local server to an MCP-compatible client and separate client configuration issues from server errors.
- You will diagnose common failures: invalid schema, garbage on stdout, wrong process path, timeout, invalid JSON, and transport mismatch.
- You will secure API keys, file paths, and data-changing operations using validation, least privilege, and explicit confirmation.
- You will design and present your own MCP server implementing a simple business process together with acceptance criteria.
Prerequisites
A computer with terminal access and the ability to install Python, uv or pip, and Node.js required for MCP Inspector. A code editor, Git, and an MCP-compatible client will be helpful. An account in a chosen vibe coding tool or access to a code-generating agent is required. It is enough to understand files, functions, JSON, and environment variables; the course explains the key parts of Python. For API exercises, you can use the provided demo service without paid keys.
Course syllabus
- The assistant knows the language, but not the customer: the boundary of the model itself
- MCP as an integration contract, not another magic plugin
- Triangle of responsibility: AI model, MCP client, and MCP server
- MCP Server, REST API, or function calling: three complete flows for one request
- Application map: documents, CRM, internal system, and external API
- Architect’s quiz: who should perform this operation?
- The tool executes intent: a customer search contract without guessing parameters
- Resource exposes content: document URI instead of a tool for everything
- The prompt controls how work is done, but does not replace permissions
- Weak vs. strong contract: complete schematics for the search_customer tool
- An error is part of the API too: not_found, invalid_input, and upstream_timeout
- Project overview: assign five capabilities to the correct MCP primitives
- Agent contract: goal, constraints, files, and acceptance test
- “Build everything” prompt versus 15-minute increment: two complete assignments
- Environment Without Version Roulette: Python, uv, Node.js, and a Pinned MCP SDK
- The first FastMCP server: one tool, one result, zero hidden magic
- stdio or Streamable HTTP: transport chosen for the runtime location
- Did the agent deliver what we ordered? Acceptance of the project structure
- Adapter boundary: why a tool should not be an HTTP client and a parser at the same time
- Agent task: add a Customer API client without changing the MCP contract
- Timeout, 404, and broken JSON: three failures a demo usually doesn’t show
- Text or structured output: comparison of full get_customer responses
- Adapter tests without internet: fixture, mock, and edge cases
- Immunity ablation: remove timeout or validation and predict the outcome
- The directory is not a knowledge base: how to represent a document, URI, and metadata
- Resource template for a document: from identifier to controlled read
- Path traversal trap: when a file name escapes the knowledge directory
- Tool search_docs: simple ranking instead of the promise of “intelligent RAG”
- Five control questions: assessing relevance, sources, and no-answer behavior
- Diagnostic quiz: index error, contract error, or model decision?
- Inspector as a laboratory client: capabilities, lists, and raw calls
- Server acceptance scenario: six calls that must pass
- When Inspector can’t see the server: transport, process command, and garbage on stdout
- Connecting to the MCP client: process and environment variable configuration
- The tool works manually, but the model does not choose it: diagnosing the name, description, and schema
- Layered quiz: client, protocol, server, or external dependency?
- The API key does not belong in the prompt or the repository
- Validation at the boundary: customer ID, date range, and forbidden fields
- Read is not write: separate permissions for search_customer and update_case
- Operation that changes data: preview, confirmation, idempotency key and audit log
- Repair prompt for an agent: symptom, log, reproduction, and ban on side refactors
- Red-team mini-test: how a model can abuse an overly broad tool
- Process selection: complaint, lead qualification, or ticket handling
- Project specification: flow diagram, MCP contracts, and acceptance criteria
- Incremental implementation: skeleton, read, search, and write
- End-to-end evaluation: success, missing data, API failure, and write denial
- Demo with a controlled failure: show more than just the happy path
- Production review: transport, authorization, observability, and trust boundaries
- Final quiz: defending the architectural decisions of your own server
FAQ
You will understand how the Model Context Protocol connects AI applications with external data, tools, and processes. Step by step, you will build an MCP server that works with an API, local documents, and a simple case register.
You do not need to build large applications from scratch, but basic code literacy will make learning easier. You will work with a programming agent: define the contract, assign small changes, run the result, and provide specific information about errors.
Vibe coding here means consciously directing an AI agent instead of blindly copying generated code. You will learn to break work into small increments, verify application behavior, and keep control over the solution.
You will build a customer support assistant that uses an external API, searches local documents, and works with a simple case register. Thanks to the shared example, you will see the entire process—from integration design to a working MCP server.
MCP is an open standard for connecting AI applications with files, databases, APIs, and other tools. The course will prepare you to build solutions that not only generate answers, but also use current context and perform concrete actions.
Yes. You will see examples of poor prompts, overly large changes, and insufficient verification. You will learn how to turn vague expectations into clear contracts and how to diagnose problems based on the system’s real behavior.
- 12 hours
- Beginner
- Certificate on completion
- Access immediately after purchase
Full course
38 USD