OneRange

MCP & Connectors in Depth

A deep course on the Model Context Protocol: how it works, building and consuming MCP servers, and its security model. Learners build a working connector that gives an AI tool safe access to a real system.

What this course covers

The course runs across 11 topics, each one a short adaptive session rather than a recorded lecture. The tutor explains the idea, works an example, checks understanding, and adjusts the next step to the answer given.

  • Introduction to MCP & Architecture Fundamentals — Explore the Model Context Protocol (MCP) architecture, understanding the relationship between clients, hosts, and servers, and how context is multiplexed.
  • The MCP Protocol Specification: Transport & JSON-RPC — Deep dive into the underlying JSON-RPC 2.0 messages, transport schemas (stdio and SSE), and the connection lifecycle.
  • Setting Up the MCP Development Environment — Configure your local environment with the necessary SDKs, CLI tools, and host configurations to build and run MCP servers.
  • Building an MCP Server: Initializing & Exposing Resources — Write your first server to initialize connections and expose read-only data resources to an AI client using the SDK.
  • Implementing Custom Tools and Prompts — Extend your server by defining executable tools with strict schemas and dynamic prompt templates that LLMs can invoke.
  • Advanced Server Logic: State Management & Session Context — Learn how to manage state, handle multi-user sessions, and maintain context across sequential LLM tool calls.
  • The MCP Security Model: Authentication & Transport Security — Analyze the threat model of MCP, covering transport-layer security, client authentication, and secure configuration management.
  • Implementing Safe Execution & Resource Sandboxing — Code defensive checks, input validation, and restricted permissions to ensure your server cannot execute malicious actions on the host system.
  • Debugging and Testing MCP Connectors — Use the MCP Inspector and mock clients to trace messages, debug connection errors, and unit test server handlers.
  • Deployment Patterns: Local Integration vs. Remote SSE — Compare deployment strategies, including packaging local stdio servers vs. deploying remote servers over Server-Sent Events (SSE).
  • Production Deployment, Logging, and Monitoring — Deploy a production-ready MCP server to a cloud environment, setting up structured logging, rate limiting, and performance monitoring.

Skills you build

Results are measured against named skills in the OneRange taxonomy of more than 10,000 skills, so a manager sees proficiency per skill rather than a completion tick.

Who it is for

Builders, engineers. The material is pitched at advanced level, and takes roughly 150 minutes at a typical pace. Because every session adapts, someone who already knows a topic moves through it quickly instead of sitting through an explanation they do not need.

It sits in the Category: Integration track of the Vero AI Catalog, and can be assigned to one person, a team, or the whole company.

How it is delivered and assessed

Delivery is conversational and interactive, including guided lab, code lab exercises. There is no video to sit through and no slide deck to click past.

Understanding is checked with a code assessment of 5 items, with a pass mark of 70%.

Administrators can copy this course into their own library and adapt it — edit the outline, change the duration, swap the assessment format, or ground it in internal documentation so answers cite the company's own source material.

Also in this track

  • Structured Outputs & JSON Mode — A focused course on making model output machine-consumable: schemas, enforcement modes, and validation layers. Learners ship a pipeline where AI output feeds software without human cleanup.
  • Tool Use & Function Calling — This course covers function calling rigorously: schema design, reliability patterns, and orchestrating multiple tools. Learners build tool-using flows that degrade gracefully instead of hallucinating actions.
  • Modern AI Stack for Builders — Master the integration layer of modern AI systems

Related

Frequently asked questions

How long does this course take?

It runs roughly 150 minutes at a typical pace. Because every session adapts, someone who already knows a topic moves through it quickly instead of sitting through an explanation they do not need.

How is it assigned to a team?

An administrator assigns it from the Vero dashboard to one person, a team, a department or the whole company, and sees progress and results per person and per skill.

Can we customise it with our own documents and terminology?

Yes. Administrators can copy this course into their own library and adapt it — edit the outline, change the duration, swap the assessment format, or ground it in internal documentation so answers cite the company's own source material.

Course code CAT-MCP.