tool-selection-framework

Tool selection depends on your codebase size, reasoning depth needs, and task type. Claude Code suits focused implementation with larger context windows; Gemini CLI excels at broad exploration. Match tool capabilities—context window, reasoning depth, cost—to your specific requirements using a systematic decision framework.

Multi-phase workflows leverage each tool's strengths: use Gemini CLI for initial exploration and broad analysis, then transition to Claude Code for detailed implementation. This approach optimizes context utilization and prevents wasted capacity while handling complex projects that span exploration and focused coding.

Evaluate codebase size (lines of code), task complexity, reasoning depth requirements, and available context windows. For medium codebases (e.g., 120K LOC), allocate tools based on phase: Gemini CLI for broad exploration, Claude Code for concentrated implementation work.

Yes. Select tools strategically based on task characteristics and constraints. Context-constrained workflows benefit from tool segmentation: reserve larger context windows for implementation tasks and use lighter tools for exploratory phases, improving overall productivity.

Task complexity—measured by reasoning depth, reasoning breadth, and implementation scope—maps directly to tool selection. High-complexity tasks requiring deep reasoning align with Claude Code; exploratory tasks with broad scope align with Gemini CLI's strengths.

Yes. The decision framework evaluates tool capabilities generically—context window, reasoning depth, cost—making it extensible for future tools. Apply the same context and complexity criteria to assess any new AI development tool against your project requirements.