SwiftLens is a Model Context Protocol (MCP) server that provides deep, semantic-level analysis of Swift codebases to any AI models. By integrating directly with Apple's SourceKit-LSP, SwiftLens enables AI models to understand Swift code with compiler-grade accuracy.
git clone https://github.com/swiftlens/swiftlens.gitSwiftLens is a Model Context Protocol server that bridges AI agents with Swift development by integrating directly with Apple's SourceKit-LSP. It provides deep semantic analysis of Swift codebases, enabling AI models to understand code structure, symbols, type information, and cross-file references with compiler-level accuracy. The tool offers 15 specialized commands for single-file analysis, cross-file symbol navigation, code modification, and project indexing. SwiftLens works with modern Swift syntax including actors, async/await, generics, and Swift Package Manager projects, requiring only macOS, Python 3.10+, and Xcode. It solves the problem of giving AI agents genuine code comprehension beyond text patterns, making it valuable for developers using Claude Code, Cursor, or other AI tools to refactor, analyze, or maintain Swift projects.
["Install and configure SwiftLens MCP server following the official documentation at [SWIFTLens_GITHUB_REPO]. Ensure SourceKit-LSP is properly set up in your development environment.","Navigate to your Swift project directory and run the analysis using your preferred AI assistant (e.g., 'Analyze the Swift codebase in [PROJECT_PATH] using SwiftLens to identify performance bottlenecks').","Review the detailed analysis report generated by SwiftLens, paying special attention to the highlighted issues and recommendations.","Implement the suggested fixes in your codebase and re-run the analysis to verify improvements.","For ongoing maintenance, integrate SwiftLens into your CI/CD pipeline to automatically analyze new code changes before merging."]
AI-assisted refactoring of Swift codebases with accurate symbol tracking
Cross-file code analysis and finding all references to a method or type
Automated code generation that understands project structure and dependencies
Code review and validation using semantic understanding of Swift syntax
No install command available. Check the GitHub repository for manual installation instructions.
git clone https://github.com/swiftlens/swiftlensCopy the install command above and run it in your terminal.
Launch Claude Code, Cursor, or your preferred AI coding agent.
Use the prompt template or examples below to test the skill.
Adapt the skill to your specific use case and workflow.
Analyze the Swift codebase in [PROJECT_PATH] using SwiftLens to identify [SPECIFIC_ANALYSIS_TYPE]. Focus on [KEY_AREAS_OF_INTEREST] and highlight any potential issues, performance bottlenecks, or architectural concerns. Provide actionable recommendations for [IMPROVEMENT_AREA].
After analyzing the Swift codebase for 'AcmeWeatherApp' located at `/projects/AcmeWeatherApp`, SwiftLens identified several critical issues and optimization opportunities in the network layer and data persistence modules. The analysis revealed that the `WeatherAPIService` class contains a memory leak in its `fetchCurrentWeather` method due to improper handling of the network response callback. The leak occurs when the API response is received but the completion handler is not properly released, causing a 15% increase in memory usage after 100 API calls. Additionally, the CoreData stack in the `WeatherPersistenceManager` has inefficient batch processing that leads to a 200ms delay when saving 1000 weather entries. The tool also detected architectural concerns in the VIPER pattern implementation, where the `WeatherInteractor` is violating the single responsibility principle by handling both business logic and data formatting. For the network layer, SwiftLens recommends implementing a retry mechanism with exponential backoff and moving the response parsing to a dedicated worker thread. For the persistence layer, it suggests batching CoreData saves and using `NSFetchedResultsController` for more efficient data fetching. The architectural issues could be resolved by splitting the `WeatherInteractor` into separate components for business logic and data formatting.
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