# Release Summary: v0.27.0
## ✅ Release Checklist
- [x] All code changes implemented
- [x] Property-based tests added (500+ tests)
- [x] MCP server integration tests passing (10 tests)
- [x] GitHub integration working
- [x] Documentation updated
- [x] Release notes prepared
- [x] Version bumped to 0.27.0
- [x] Lint checks passing (`make lint`)
- [x] SATD removed (fixed TODO comment)
- [x] Low complexity verified (max cyclomatic: 7)
- [x] Git commit created
- [x] Pushed to GitHub
- [x] GitHub release created: https://github.com/paiml/paiml-mcp-agent-toolkit/releases/tag/v0.27.0
- [x] Crates.io dry-run successful
- [x] All tests passing (fixed cache consistency + SARIF tool name tests)
## 📊 Quality Metrics
- **SATD**: 0 items (was 1, fixed)
- **Max Cyclomatic Complexity**: 7 (well below threshold)
- **Max Cognitive Complexity**: 17 (acceptable)
- **Test Coverage**: Comprehensive property tests added
- **Lint Status**: All checks passing
## 🚀 Major Features Delivered
1. **Stateful MCP Server**
- Persistent refactoring sessions
- JSON-RPC API with 4 methods
- Cap'n Proto schema ready
- Thread-safe state management
2. **GitHub Issue Integration**
- `--github-issue` flag added
- Intelligent keyword extraction
- Enhanced AI context
3. **Property-Based Testing**
- 6 components covered
- 500+ test cases
- Edge cases discovered and fixed
## 📁 Files Changed Summary
- **New Files**: 20+
- **Modified Files**: 25+
- **Deleted Files**: 70+ (cleaned up old todo/bug files)
- **Total Changes**: 9,018 insertions, 40,440 deletions
## 🔄 Next Steps for Publishing
To publish to crates.io:
```bash
cd server
cargo publish --no-verify
```
Note: The `--no-verify` flag is needed due to vendor assets being downloaded during build.
## 🎉 Success!
All three specifications have been successfully implemented, tested, and released. The pmat toolkit now features:
- Robust property-based testing
- Stateful MCP server for complex workflows
- Intelligent GitHub issue integration
The implementation maintains high code quality standards with zero SATD and low complexity metrics.