Enterprise MCP: Build Secure Model Context Protocol Servers

About this course

Your team connects an AI assistant to an internal API using MCP. The demo works perfectly.

Then the security team asks four questions:

• Who is calling?
• What are they allowed to do?
• Who approved the action?
• Can we audit what happened?
• Building an MCP server is one thing. Building one with the security controls needed in an enterprise environment is another.

The Model Context Protocol (MCP) is the open standard for connecting AI applications to tools, APIs and data. You build an integration once, and any MCP-compatible client can use it: Cursor, Claude Desktop, the OpenAI API or your own AI agents.

In this hands-on Python course, you'll learn MCP from the fundamentals, build your own servers with the official MCP Python SDK, and add the security controls an enterprise expects.

In the final section, you secure a multi-user MCP task tracker with user identity, tool-level permissions, human approval and structured audit logging.

What you'll build and learn1. Understand the Model Context ProtocolMCP architecture: hosts, clients, servers, tools, resources and promptsJSON-RPC communication and the stdio and Streamable HTTP transports

Stateless MCP servers: how each request carries everything the server needs

Why one MCP server can serve many compatible AI applications2. Connect MCP to real AI applications

Configure and test MCP integrations with Cursor and Claude Desktop. Explore ready-made servers for browser automation with Playwright, web search and remote services such as DeepWiki.

Learn how AI hosts are configured to start local MCP servers and connect to remote ones.3. Build MCP servers in Python

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