MCP Test MCP

--- story_id: "0022" title: "Performance optimization and profiling" created: "2025-10-09" status: "backlog" dependencies: ["0003", "0005", "0006", "0007", "0008"] estimated_complexity: "medium" tags: ["performance", "optimization", "phase3"] --- # Story 0022: Performance optimization and profiling ## Description Profile and optimize mcp-test-mcp for faster responses, reduced memory usage, and better handling of large result sets. This ensures good developer experience even with complex servers. ## Acceptance Criteria - [ ] Performance profiling completed for all operations - [ ] Response time optimization (target: <200ms for simple operations) - [ ] Memory usage optimization (target: <50MB steady state) - [ ] Large schema handling (servers with 100+ tools) - [ ] Connection pooling for repeated operations - [ ] Lazy loading for large result sets - [ ] Performance regression tests - [ ] Performance benchmarks documented - [ ] Optimization recommendations for users ## Technical Notes **Performance targets:** - Connection establishment: <5s - list_tools: <1s for 100 tools - call_tool: <500ms overhead (excluding actual tool execution) - Memory footprint: <50MB for typical usage **Optimization areas:** 1. Connection reuse (avoid reconnecting) 2. Schema caching (if schema doesn't change) 3. Response streaming for large results 4. Async operation optimization 5. Memory efficient data structures **Profiling tools:** - cProfile for CPU profiling - memory_profiler for memory analysis - pytest-benchmark for regression testing ## AI Directives **IMPORTANT**: As you work through this story, please mark checklist items as complete `[x]` as you finish them. This ensures that if we need to pause and resume work, we have a clear record of progress. Update the `status` field in the frontmatter when moving between stages (in-progress, ready-for-review, done, blocked). Profile first, optimize second. Don't optimize prematurely - focus on actual bottlenecks.