About
A Model Context Protocol server that integrates with Cursor and Chrome to detect, analyze, and suggest fixes for Structurizr DSL syntax errors in real time, with error history and configurable ports.
Capabilities
Structurizr DSL Debugger for Cursor
The Structurizr DSL Debugger is an MCP‑enabled server that bridges the gap between a developer’s local workspace and the Structurizr Lite environment. It targets teams that model software architecture using the Structurizr DSL language within the Cursor IDE, providing instant feedback on syntax errors and actionable suggestions to fix them. By exposing a set of MCP tools—, , , and —the debugger turns a traditionally manual debugging workflow into an automated, interactive experience that runs alongside the AI assistant.
What problem does it solve?
When writing large Structurizr DSL files, subtle syntax mistakes (missing braces, wrong keyword order, or misplaced identifiers) can cause the entire model to fail rendering. Traditionally developers would have to manually run Structurizr, inspect error logs in a browser console, and hunt down the offending line. This process is time‑consuming and interrupts focus. The debugger captures errors in real time, correlates them with the exact source line, and surfaces concise explanations. It eliminates context switching between IDE, browser, and command line, allowing developers to stay within Cursor while receiving immediate guidance.
How the server works
The MCP server spins up a lightweight Node.js process that listens for tool calls from Cursor. It connects to a running instance of Structurizr Lite (via a configurable port) and uses Chrome’s remote debugging protocol to monitor the rendering pipeline. When a DSL file is saved, the server re‑parses it and returns any syntax errors detected. Developers can then request a history of recent errors or ask the server to apply an automated fix—such as inserting a missing keyword or correcting a typographical error. All interactions are carried out through JSON messages, keeping the integration language‑agnostic and easily consumable by AI assistants.
Key capabilities
- Real‑time error detection – Errors are reported instantly as soon as a file is modified, reducing the time between mistake and correction.
- Intelligent fix suggestions – The server analyses common DSL patterns to propose precise, minimal changes that resolve the issue.
- Browser integration – By attaching to Chrome’s debugging port, it can read the live rendering state and detect errors that only surface during model execution.
- Cursor IDE integration – The MCP tools are designed to fit seamlessly into Cursor’s command palette, so developers can invoke them without leaving the editor.
- Configurable ports – Support for custom Structurizr and Chrome debugging ports makes the tool flexible in diverse deployment environments.
- Error history – A persistent log of captured errors allows teams to review trends, track recurring problems, and refine their DSL templates.
Use cases & real‑world scenarios
- Rapid prototyping – Teams iterating on architecture diagrams can validate DSL syntax on the fly, accelerating design cycles.
- Educational settings – Instructors can demonstrate correct DSL usage while students receive instant feedback on mistakes.
- Continuous integration – Integrate the debugger into CI pipelines to catch syntax errors before a model is deployed or published.
- AI‑assisted coding – An AI assistant can query the debugger to confirm whether a suggested snippet will compile, ensuring higher confidence in generated code.
- Legacy model migration – When porting old DSL files to newer Structurizr versions, the debugger flags deprecated syntax automatically.
Unique advantages
Unlike generic linters or external error checkers, the Structurizr DSL Debugger is tightly coupled with both the Cursor IDE and the live browser rendering engine. This dual integration means it can detect errors that only manifest during model evaluation, not just static parsing failures. Its MCP‑based API makes it natively compatible with AI assistants that can invoke tools via the Model Context Protocol, enabling a truly conversational debugging experience. By providing actionable fixes rather than merely pointing out problems, it empowers developers to correct issues in a single click, dramatically reducing turnaround time.
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