Model Context Protocol (MCP) Deep Dive
capability building,
designed for your organisation.
A custom-built corporate programme for AI engineers, senior software engineers, platform engineers, backend developers, and solution architects (3+ years) building portable AI integrations across Claude, ChatGPT, Cursor, and other MCP clients. We design the curriculum around your tech stack, project archetypes, and target business outcomes — delivered by domain-expert trainers and reinforced through AI-evaluated assessments.
A modular syllabus, built to be tailored.
Below is our reference curriculum. Every syllabus we deliver is tailored to your customer-specific requirements — module depth, sequencing, lab environments, and capstone projects are adapted to your team's starting point, tech stack, and target outcomes.
- The integration explosion problem MCP addresses: M tools × N hosts → M+N
- MCP's design choices: JSON-RPC, primitives (tools, resources, prompts), capability negotiation
- MCP vs. function calling vs. plugins vs. extensions — the conceptual differences
- Why MCP is gaining adoption beyond Anthropic — ChatGPT, Cursor, others
Want the full module-by-module syllabus, sample assignments, and pricing?
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Demonstrable skills your team will apply on live projects.
Build production-grade MCP servers
From local prototype to containerised, authenticated, observable enterprise service.
Design for cross-client portability
Servers that work cleanly across Claude Desktop, ChatGPT, Cursor, custom hosts — without client-specific quirks.
Apply MCP security patterns
Prompt-injection defence, capability scoping, sandboxing, RBAC, audit logging.
Pass GSDC MCP Engineer certification
Two attempts; cohort first-attempt pass rate 84%.
Ship production MCP capstone
Enterprise-grade MCP server consumed by 2+ MCP hosts, with full observability and security.
Lead MCP integration strategy
Equipped to drive MCP adoption strategy in your organisation — server registry, governance, ecosystem participation.
Where your team is now vs where they'll be after the programme.
Where most teams start
- ·Aware of MCP as Anthropic's emerging standard but unable to build, test, or deploy MCP servers
- ·Limited fluency with the MCP specification — capabilities, primitives (tools, resources, prompts), transport mechanisms
- ·No experience with the official SDKs (Python, TypeScript, Java, C#)
- ·Cannot design MCP servers for cross-client portability — Claude Desktop vs. ChatGPT vs. Cursor have different consumption patterns
- ·Unfamiliar with MCP security implications: prompt injection via tool outputs, capability scoping, sandboxing
- ·No production deployment experience — local MCP works, but enterprise-scale MCP requires additional engineering
Where they'll arrive
- ✓MCP specification mastery — fluent in the full spec including capabilities, tools, resources, prompts, sampling, and roots
- ✓SDK fluency — productive in TypeScript and Python MCP SDKs; aware of Java/C# alternatives
- ✓Cross-client portability — designs MCP servers that work across Claude Desktop, Claude Code, ChatGPT, Cursor, custom hosts
- ✓Production deployment — ships MCP servers as containerised, observable, secured services with proper authentication
- ✓Security discipline — applies prompt-injection defence, capability scoping, sandboxing, and audit logging
- ✓Ecosystem awareness — knows the MCP server ecosystem and can build on or extend existing servers
Built for L&D outcomes, not seat counts.
Prompt discipline, not prompt luck
Learners move from trial-and-error prompting to named patterns such as role prompting, few-shot, prompt chaining, and self-critique.
Reusable team assets
The programme produces Custom GPTs, reusable workflow templates, and a shared prompt library that teams can govern and scale.
Daily productivity workflows
Labs focus on email, reports, slides, meetings, spreadsheets, research synthesis, and role-based business assignments.
Measured time savings
Capstone workflows document recurring task compression, review-cycle reduction, and before/after productivity improvements.
Responsible enterprise use
Learners practise confidentiality, IP, bias detection, verification checklists, and safe-use protocols before adoption at scale.
Sustainment built in
30-day, 60-day, and 90-day check-ins help learners keep pace as ChatGPT features and frontier models evolve.
A four-milestone path from skill gap to client-ready.
Foundation & baseline
Establish a working mental model of ChatGPT, frontier models, tokens, context windows, hallucination risks, and model-selection trade-offs.
Prompt engineering labs
Learners practise CRISPE, SPEAR, role prompting, constraint-led prompting, few-shot prompting, self-critique, and prompt iteration on real work scenarios.
Custom GPTs & workflow automation
Each learner builds reusable GPTs and connects ChatGPT to productivity tools for email, documents, spreadsheets, meetings, and research workflows.
Capstone & sustainment
Learners demonstrate a personal AI productivity system and continue with prompt-of-the-week, model-of-the-month, and 30/60/90-day check-ins.
Want this curriculum aligned to your tech stack and project archetypes?
Why enterprise teams choose the B2B engagement model.
Domain-expert trainers, not professional presenters.
"My job isn't to teach ChatGPT as a tool — it's to help professionals build repeatable AI workflows, verify the output, and reclaim hours from routine work."
Taught by people who've actually shipped the work.
Built for L&D leaders and their learners.
Who this is for
- ·Knowledge workers who want to apply ChatGPT productively in their daily workflows
- ·Business analysts, consultants, marketing professionals, project managers, and individual contributors
- ·Teams that use ChatGPT for occasional drafting but need reliable, business-grade outputs
- ·Managers looking to establish team-wide prompt standards and safe-use protocols
- ·Organisations that want to automate repetitive work across email, spreadsheets, calendars, and documents
Pre-requisites
- ·No coding prerequisite for business and productivity tracks
- ·Basic familiarity with workplace tools such as email, documents, spreadsheets, slides, and meetings
- ·Willingness to bring real recurring tasks into labs for workflow redesign
- ·Enterprise cohorts should align data-handling expectations before learners use company or client information
Trusted by L&D leaders across the world.
"The programme moved our team from random prompting to a repeatable method. The prompt library and Custom GPTs became assets we could actually reuse."
"The most useful part was workflow automation. Learners took their weekly reports, meeting recaps, and research tasks and reduced hours of repetitive effort."
"Responsible use was handled practically. The team finally understood what can be pasted, what must be masked, and how to verify output before sending it."
Questions L&D teams ask before signing.
MCP stands for Model Context Protocol. It is an open-source standard that allows AI applications to connect with external tools, files, databases, APIs, workflows, and enterprise systems. MCP was created at Anthropic by David Soria Parra and Justin Spahr-Summers.