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Model Context Protocol (MCP) and Custom Tooling

Model Context Protocol (MCP) and Custom Tooling

Learn how to extend Cascade's capabilities using the Model Context Protocol (MCP) to integrate custom tools and unlock organization-specific workflows.

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Throughout this course, we’ve seen Cascade perform incredible feats. It can search your codebase, run commands in your terminal, and even browse the public web for information. It has a powerful, built-in toolkit.

But what happens when you need it to do something unique to your world? What if you need it to look up a ticket in your company’s private Jira instance? Or query a proprietary product database? Or check the status of a build on your internal CI server?

This is the Walled Garden problem. The AI is immensely powerful inside its garden, your local codebase, the public web, but it’s unaware of the vast, private landscape of tools and services that make up your company’s unique ecosystem. How do you build a gate in that wall? How do you give your AI co-pilot secure, controlled access to your team’s specific power tools?

The answer is the Model Context Protocol (MCP). This lesson is about breaking down the walls of the default toolkit and learning how to extend Cascade’s capabilities in almost limitless ways.

Demystifying MCP: An app store for your AI

Before diving into the technical details, let’s use a simple analogy. Think of Cascade as a brand-new iPhone. Out of the box, it’s a powerful platform with fantastic built-in apps like a file search tool (like the Contacts app) and a terminal runner (like the Phone app). But its true power comes from the App Store, which allows developers worldwide to build new apps that give the iPhone new capabilities.

MCP is the protocol that enables an App Store for your AI.

  • Model Context Protocol (MCP): This is the common language, the technical standard, that allows AI and custom tools to communicate securely and effectively.

  • MCP server: This is the custom tool, the app in our analogy. It’s a separate program you or your team can build and run that listens for requests from the AI. For example, you could have a JiraServer, a GitHubServer, or a CompanyDatabaseServer.

  • MCP client: This is Cascade. The AI agent can automatically discover ...