feat: implement OGC API Features Items endpoint with bbox filtering (#17)

[mikemcdougall]

Summary

• Implements OGC API Features Items endpoint (GET /ogc/features/collections/{collectionId}/items)
• Adds comprehensive bbox parameter support for spatial filtering
• Ensures DRY compliance by reusing existing FeatureServer infrastructure
• Provides complete test coverage with 11 passing OGC integration tests

Technical Implementation

• Shared Query Pipeline: Reuses IFeatureStore and IQueryFormatter from FeatureServer
• OGC Compliance: Supports standard bbox format (minx,miny,maxx,maxy)
• Proper Content Types: Returns application/geo+json for GeoJSON responses
• Spatial Filtering: Implements WKB polygon generation for bbox queries
• Error Handling: Comprehensive validation with appropriate HTTP status codes

Test Coverage

✅ All 11 OGC API Features tests passing:

• Landing page and conformance endpoints
• Collections listing and metadata
• Items endpoint with bbox filtering
• Error scenarios (invalid bbox, missing collections)

Architecture Compliance

• No DRY violations: Shared infrastructure prevents code duplication
• Vertical slice organization: OGC features grouped cohesively
• Dependency limits: Follows architectural constraints
• AOT compatibility: Uses proper JSON contexts

Production Notes

• Current WKB generation is simplified for MVP
• TODO: Replace with NetTopologySuite for production geometry handling
• Spatial filtering works correctly for test scenarios

🤖 Generated with Claude Code

[github-actions]

🤖 LLM Architecture Review

✅ Assessment: APPROVED

🏗️ Architecture Review Summary

✅ Good Patterns Found:

• Comprehensive XML documentation on public types in OgcModels.cs and OgcFeaturesEndpoints.cs, ensuring good API documentation and maintainability.
• Effective use of Minimal API patterns in OgcFeaturesEndpoints.cs, aligning with the project's architectural direction to avoid the controller-based model.
• Proper dependency injection and service configuration in Program.cs, demonstrating clean architecture principles by isolating infrastructure setup from business logic.
• Extensive test coverage in OgcFeaturesEndpointTests.cs, including detailed integration tests that validate both functionality and compliance with OGC standards.

⚠️ Architecture Concerns:

• Performance Concerns: Usage of Task.Result or similar blocking calls were not found, which is good. However, the manual WKB creation in OgcFeaturesEndpoints.cs might be optimized or replaced with a more efficient library to avoid potential performance bottlenecks.

💡 Recommendations:

• Optimize Spatial Data Handling: Consider using a library like NetTopologySuite for handling spatial data to improve performance and accuracy. Replace the placeholder WKB generation with a robust implementation.

  // Example using NetTopologySuite for better WKB handling
  var geometryFactory = new NetTopologySuite.Geometries.GeometryFactory(new NetTopologySuite.Geometries.PrecisionModel(), 4326);
  var envelope = new NetTopologySuite.Geometries.Envelope(minX, maxX, minY, maxY);
  var polygon = geometryFactory.ToGeometry(envelope);
  var wkbWriter = new NetTopologySuite.IO.WKBWriter();
  var wkb = wkbWriter.Write(polygon);

📚 Educational Notes:

• Why Minimal APIs Matter: By using Minimal APIs, the project benefits from reduced boilerplate, clearer routing and endpoint definitions, and improved testability. This approach aligns with modern .NET practices and helps maintain clean architecture by keeping the infrastructure and business logic decoupled.
• Importance of Proper Spatial Data Handling: Efficient handling of spatial data is crucial in geospatial applications for performance and accuracy. Using specialized libraries like NetTopologySuite ensures that spatial operations are performed correctly and efficiently, leveraging years of optimization and industry standards.

Overall Assessment: APPROVED

The code review reveals a well-structured approach adhering to the project's architectural standards. The use of Minimal APIs, comprehensive testing, and adherence to OGC standards are particularly commendable. While there are recommendations for optimizing spatial data handling, the current implementation meets the project's critical architectural requirements.

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Automated architectural analysis powered by OpenAI GPT-4
This review focuses on architectural patterns and design decisions
Human review still recommended for complex changes