Examples

Sequa MCP supercharges your favorite AI tools—whether it’s Cursor, a VS Code extension, or a custom script—by providing them with deep, workspace-wide context. This allows you to solve complex problems that span multiple repositories, services, and domains right from your editor.

Below are a few examples of what’s possible when your AI assistant is powered by Sequa.

1. The Cross-Service Bug Hunt

Problem: You’re debugging an issue where a null value is causing an error in your billing-service. The stack trace gives you a line number, but you suspect the bad data originates upstream in the user-service. Investigating this typically means manually cloning another repository and painstakingly tracing the data flow back from the point of impact.

Solution: With Sequa MCP, you can investigate from a single file. Ask your AI assistant to trace the origin of the problematic data across service boundaries. Sequa provides the context from both repositories, allowing the assistant to identify the exact code path in the user-service that could lead to the null value being passed.

2. Replicating UI Components Across Repositories

Problem: You are building a new marketing site in a new repository (new-website-2024) and need to create a “pricing tier” component. Your company’s main application (monolith-app) has a well-designed, user-approved pricing component, but it was built years ago with different technologies (e.g., SCSS modules). You need to replicate its structure and styling using your new stack (e.g., React with Tailwind CSS).

Solution: Ask your AI assistant to act as a bridge between the two projects. Instruct it to find the pricing component in the old monolith-app repository, analyze its structure and styling, and generate the equivalent component for you using modern best practices and your current tech stack.

3. Generating a Client to Consume a New API Endpoint

Problem: A backend engineer has just deployed a new endpoint in the api-service for fetching a user’s activity feed. As a frontend developer working in the ios-app repository, you need to consume it. The OpenAPI specification hasn’t been updated yet, and you don’t want to break your flow by digging through the backend codebase to find the correct data transfer objects (DTOs) and endpoint path.

Solution: Let your AI assistant write the client code for you. Ask it to look inside the api-service repository, find the new activity feed endpoint, inspect its request and response shapes, and generate a typed client function in Swift for you to use in your iOS app.

4. Architectural Onboarding for New Teammates

Problem: A new engineer is starting their first task, which involves adding a field to a data export in the reporting-service. They have no context on the architecture and need to understand: What triggers a report? Does it happen synchronously? Where does the data come from? Who are the consumers?

Solution: They can simply ask their AI assistant for an architectural overview. Sequa MCP provides the assistant with access to all your indexed documentation, including Live Docs and system diagrams. The assistant can synthesize this information into a concise, easy-to-understand summary, complete with links to the most relevant documentation and key source files.