Model Context Protocol Training Platform

Train GPT. Master MCP. Build Agents.

The definitive platform for learning Model Context Protocol — the open standard powering the next generation of AI agent integrations.

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// Protocol Overview

What is
MCP?

Model Context Protocol (MCP) is an open standard introduced by Anthropic in November 2024. Think of it as the USB-C portUniversal connector for AI —
one protocol, all data sources for AI applications.

Before MCP, every time a developer wanted an AI assistant to access external data — a company wiki, a code repo, a database — they had to write a custom connector. This didn't scale.

MCP eliminates this by providing a universal, open standard that lets any compliant AI plug into any data source or tool in a consistent, secure way.

The protocol defines three core primitives: Tools (actions AI can invoke), Resources (data the AI can read), and Prompts (reusable workflow templates).

MCP
🗄️
Database
📁
Files
🌐
APIs
💬
Chat
⚙️
Tools
📋
Prompts

// Architecture

How MCP Works

01
Host App
Claude Desktop, VS Code, or your custom AI app acts as the MCP host.
02
MCP Client
The client inside your host maintains a 1:1 connection with each server.
03
Protocol Layer
JSON-RPC 2.0 over stdio or SSE transport handles all communication.
04
MCP Server
Lightweight servers expose tools, resources and prompts to the LLM.
05
Data Sources
Files, databases, APIs, services — anything your agent needs to act.

// Interactive Demo

Build Your First
MCP Server

server.py — MCP Server
from fastmcp import FastMCP import httpx # Initialize your MCP server mcp = FastMCP("GPT Training Hub") @mcp.tool() async def search_docs(query: str) -> dict: """Search training documentation.""" # Your search logic here return {"results": [], "query": query} @mcp.resource("docs://training/{module}") async def get_module(module: str) -> str: """Retrieve a training module.""" return load_module_content(module) if __name__ == "__main__": mcp.run(transport="stdio")
import { McpServer } from "@modelcontextprotocol/sdk"; const server = new McpServer({ name: "gpt-training-hub", version: "1.0.0", }); server.tool("search_docs", { description: "Search training docs", inputSchema: { type: "object", properties: { query: { type: "string" } } } }, async ({ query }) => { return { content: [{ type: "text", text: `Searching: ${query}` }] }; }); await server.connect(new StdioTransport());
{ "mcpServers": { "gpt-training-hub": { "command": "python", "args": ["server.py"], "env": { "API_KEY": "your-api-key", "DEBUG": "false" } }, "filesystem": { "command": "npx", "args": [ "-y", "@modelcontextprotocol/server-filesystem", "/Users/you/projects" ] } } }
Terminal — Output
[ready]Click "Run Demo" to see MCP in action →

// Applications

What Can You Build?

🧠
Knowledge Base Agents
Connect GPT to your internal docs, wikis, and databases. Answer questions using live company knowledge instead of stale training data.
Resources
⚙️
Workflow Automation
Trigger Jira tickets, update CRM records, post to Slack — all from a natural language conversation. MCP handles the write operations safely.
Tools
🔍
Deep Research Agents
Build ChatGPT Deep Research integrations. Expose search and fetch tools so your data becomes part of comprehensive AI research reports.
Search + Fetch
💻
Dev Tooling
Integrate GitHub, file systems, and CI/CD pipelines. Let your AI write, test, and deploy code with full context of your codebase.
Filesystem
🗣️
Conversational CRM
Connect Salesforce or HubSpot. Your AI handles lead enrichment, follow-ups, and pipeline updates through natural dialogue.
Write Ops
🎨
Creative Pipelines
Chain Figma, Canva, and ElevenLabs MCP servers to create multi-step creative workflows: design, refine, and generate audio in one session.
Multi-Server

// Curriculum

Your Learning Path

01
MCP Foundations
Protocol overview, architecture, hosts, clients and servers
Beginner
02
Tools & Resources
Define callable tools, expose data resources, URI schemes
Beginner
03
Transport Protocols
stdio vs SSE, JSON-RPC 2.0, session lifecycle management
Intermediate
04
Building with FastMCP
Python & TypeScript SDKs, decorators, error handling patterns
Intermediate
05
Security & Auth
OAuth 2.0 integration, prompt injection defense, safe write ops
Advanced
06
Production Multi-Agent Systems
Orchestration, concurrency, LangChain/LlamaIndex integration, deployment
Expert

// Knowledge Check

Test Your MCP Knowledge

1 / 5
0/5

// Reference

MCP Glossary

Model Context Protocol (MCP)
Open standard by Anthropic for connecting AI systems to external data and tools via a universal interface. Announced November 2024.
Tool
A callable function exposed by an MCP server. Tools enable the AI to take actions — searching, writing, updating records, triggering workflows.
Resource
A data endpoint exposed via a URI (e.g., file:///path or db://table/row). Resources are read-only and provide context to the model.
Prompt
Predefined workflow templates that servers can expose. User-controlled prompt blueprints for orchestrating complex, repeatable interactions.
Host
The AI application that embeds an MCP client — e.g., Claude Desktop, VS Code Copilot, or a custom LLM-powered app.
MCP Server
A lightweight process that exposes tools, resources, and prompts. Servers run locally or remotely and communicate via stdio or SSE transport.
stdio Transport
Standard input/output transport. The client spawns the server as a subprocess and communicates via stdin/stdout. Used for local servers.
SSE Transport
Server-Sent Events over HTTP. Used for remote MCP servers. Supports streaming and enables cloud-deployed, internet-accessible MCP services.
JSON-RPC 2.0
The underlying message format MCP uses. Enables bidirectional communication: clients call methods on servers, servers send notifications back.
FastMCP
A high-level Python framework for building MCP servers quickly. Provides decorators like @mcp.tool() and @mcp.resource() for clean, Pythonic server code.