SwiftLens-MCP: Accurate Swift Symbol Extraction

In the ever-evolving landscape of software development, accurate code analysis is paramount. For Swift developers, precisely understanding and extracting symbols from their codebase can be a significant challenge. Historically, approaches relying on regular expressions, while convenient, often fall short in capturing the nuanced structure of Swift code. This led to the removal of regex-based symbol extraction in version 0.3.0. However, the demand for true precision in Swift symbol lookup persists. This is where the integration of SwiftLens-MCP emerges as a powerful solution, promising AST-based accuracy that regex simply cannot match.

The Power of AST-Based Parsing with SwiftLens-MCP

At the heart of SwiftLens-MCP's capability lies its reliance on Abstract Syntax Trees (ASTs). Unlike regex, which looks at code as a sequence of characters, ASTs represent the grammatical structure of the code itself. This means SwiftLens-MCP, powered by the robust SwiftSyntax library, can parse Swift code with 100% accuracy. It doesn't just guess; it understands the code's components precisely. This allows for the extraction of rich symbol information, including data types, protocols, functions, properties, and their visibility modifiers (public, private, internal, etc.). Whether you're working on a small utility or a large-scale application, SwiftLens-MCP offers the depth of analysis required. Furthermore, its cross-project analysis capabilities mean you can gain insights across multiple codebases simultaneously, providing a holistic view of your project's architecture and dependencies. The fact that SwiftLens-MCP is actively developed and available through the PromptPing marketplace ensures that it remains a cutting-edge tool, continually improved and supported. This commitment to development means you can rely on it not just for today's needs but also for the future challenges of Swift development. The precision offered by AST-based parsing is not just a technical advantage; it translates directly into tangible benefits for developers, saving time, reducing errors, and fostering a deeper understanding of complex codebases. When you need to be absolutely sure about the symbols in your Swift code, SwiftLens-MCP is the tool that delivers that confidence.

Seamless Integration: The swift_symbols Tool

To make this powerful analysis accessible within existing workflows, we propose an optional tool named swift_symbols. This tool acts as a direct interface to SwiftLens-MCP's symbol extraction capabilities. Crucially, this tool is only available if SwiftLens-MCP is installed on the user's system, ensuring that the environment is properly set up for accurate parsing. The swift_symbols tool is designed with flexibility and ease of use in mind. Its input schema is straightforward, allowing users to specify the filePath of the Swift file they wish to analyze. Beyond just the file path, it offers a symbolType parameter, enabling users to filter the extraction results. This enum-based filtering includes options like 'all' for a comprehensive extraction, or more specific types such as 'classes', 'protocols', and 'functions'. This granular control allows developers to pinpoint exactly the information they need, streamlining their analysis process. The integration pattern is elegantly handled by an actor named SwiftLensIntegration. This actor encapsulates the logic for calling the SwiftLens-MCP tool. The extractSymbols function within this actor first checks for the presence of SwiftLens-MCP using isSwiftLensInstalled(). If the tool is not found, it throws a SwiftLensError.notInstalled, providing clear feedback to the user. Upon successful detection, it proceeds to call the SwiftLens-MCP tool via `callSwiftLensTool(