Optimizes AI coding tools by minimizing token usage when parsing Swift code and log files for iOS development.
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Okanga is a custom Model Context Protocol (MCP) server designed to supercharge AI coding assistants, particularly for iOS/Swift development with Claude. Unlike standard file system tools, Okanga is token-aware, providing specialized utilities to analyze Swift code structure, search projects, and diagnose Xcode build issues without overwhelming the LLM's context window with excessive data. This innovative approach dramatically reduces token consumption, making AI-powered code analysis and debugging more efficient and cost-effective.
Key Features
01Token-aware parsing of Swift files, returning only class definitions, properties, and function signatures.
02Extraction of resolved Xcode build settings and fetching recent build logs for diagnostics.
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04Ability to read specific line ranges from files to minimize context.
05Specialized understanding and analysis of Xcode project and workspace structures.
06Server-side project searching using `grep` to find code examples.
Use Cases
01Searching for code examples and usage patterns within large projects using an AI assistant without ingesting entire files.
02Diagnosing and resolving Xcode build errors with AI assistance by fetching relevant logs and settings.
03Efficiently analyzing Swift codebases with LLMs while minimizing token usage.
