Code-Index-MCP

# Updated MCP vs Native Performance Analysis Report **Date**: January 6, 2025 **Test Coverage**: 42 out of 80 tests completed (52.5%) **Repositories Tested**: Go (gin), Python (Django), JavaScript (React), Rust (tokio) ## Executive Summary After completing 42 performance tests comparing MCP (Model Context Protocol) indexed search against native command-line tools, the results show significant performance variations by repository type and language. ### Key Findings 1. **Overall Performance**: Native tools are 2.5x faster on average than MCP 2. **Token Efficiency**: Native tools use 71% fewer tokens than MCP 3. **Success Rates**: Native (87%) vs MCP (58%) 4. **Repository-Specific Performance Varies Dramatically** ## Detailed Performance Metrics ### By Repository #### Go (gin) - 15 tests - **MCP Performance**: 1,970ms average, 10,100 tokens - **Native Performance**: 2,497ms average, 4,875 tokens - **Winner**: MCP is 1.3x faster - **Note**: MCP performs better for Go codebases #### Python (Django) - 11 tests - **MCP Performance**: 4,400ms average, 8,767 tokens - **Native Performance**: 425ms average, 1,525 tokens - **Winner**: Native is 10.4x faster - **Note**: Native tools excel with Python's readable syntax #### JavaScript (React) - 9 tests - **MCP Performance**: 6,000ms average, 7,850 tokens - **Native Performance**: 596ms average, 1,843 tokens - **Winner**: Native is 10.1x faster - **Note**: Large React codebase favors native grep/find #### Rust (tokio) - 7 tests - **MCP Performance**: 875ms average, 10,750 tokens - **Native Performance**: 960ms average, 1,660 tokens - **Winner**: MCP is 1.1x faster (but 0% success rate) - **Note**: MCP failed all Rust tests due to tool availability ## Success Rate Analysis ### MCP Success Rates by Repository - Go: 60% (3/5 tests) - Python: 100% (3/3 tests) - JavaScript: 50% (1/2 tests) - Rust: 0% (0/2 tests) ### Native Success Rates by Repository - Go: 70% (7/10 tests) - Python: 88% (7/8 tests) - JavaScript: 100% (7/7 tests) - Rust: 100% (5/5 tests) ## Performance by Query Category Based on completed tests: ### Symbol Searches - Native excels at finding specific symbols (functions, classes) - Average native time: ~400ms - Average MCP time: ~2,000ms ### Navigation Queries - Both perform well for file dependency queries - Native: ~600ms average - MCP: ~1,500ms average (when successful) ### Semantic/Understanding Queries - Native requires more complex grep patterns - MCP theoretically better but often fails - Native: ~1,200ms average - MCP: ~4,000ms average ## Tool Usage Patterns ### Native Tools - Most common: Grep (used in 90% of tests) - Secondary: Bash, Find, Glob - Average tools per query: 2.1 ### MCP Tools - Primary: mcp__code-index-mcp__search_code - Secondary: mcp__code-index-mcp__symbol_lookup - Average tools per query: 2 (when successful) ## Recommendations ### When to Use Native Tools 1. **Python Projects**: 10x performance advantage 2. **JavaScript/Web Projects**: 10x performance advantage 3. **Quick symbol lookups**: Faster and more reliable 4. **Token-constrained environments**: 71% fewer tokens ### When to Use MCP 1. **Go Projects**: 1.3x performance advantage 2. **Complex semantic queries**: Better understanding (when working) 3. **Cross-file relationship analysis**: More sophisticated ### Critical Issues with MCP 1. **Availability**: 42% failure rate due to tool availability 2. **Consistency**: Performance varies wildly by language 3. **Token Usage**: 3.5x more tokens on average ## Conclusion While MCP shows promise for certain use cases (Go projects, semantic understanding), native tools currently provide: - More consistent performance - Higher reliability (87% vs 58% success) - Significantly lower token usage - Better performance for 2 out of 4 tested languages For production use, a hybrid approach is recommended: - Use native tools as the primary search method - Fall back to MCP for complex semantic queries - Consider language-specific strategies ## Next Steps 1. Complete remaining 38 tests for full dataset 2. Investigate MCP tool availability issues 3. Create language-specific optimization strategies 4. Develop hybrid search implementation