chore: Update ToolHive API models

[stackloke2e]

Summary

This PR updates the ToolHive API models generated from the latest OpenAPI specification.

Changes

• Updated Pydantic models in src/mcp_optimizer/toolhive/api_models/

Notes

• This PR was automatically generated by the update-thv-models workflow
• The models are generated using datamodel-codegen from ToolHive's OpenAPI endpoint

🤖 Generated with GitHub Actions

[claude]

PR Review: Update ToolHive API Models

This PR updates auto-generated Pydantic models from the latest ToolHive OpenAPI specification. Overall, the changes look good with proper code organization improvements.

✅ Positive Changes

Better Code Organization

• Refactored registry.py (274 lines) into smaller, focused modules:
  • remote.py (39 lines) - Remote auth configuration
  • tokenexchange.py (40 lines) - OAuth token exchange config
  • types.py (+268 lines) - Shared types consolidation
• This improves modularity and maintainability

Proper Dependency Management

• New imports properly added to runner.py:11-21 for the refactored modules
• Field references correctly updated (registry.EnvVar → types.EnvVar, registry.Header → types.Header)

New Features Added

• remote.Config.resource (line 32-34) - OAuth 2.0 resource indicator (RFC 8707)
• types.OAuthConfig.resource (line 107-108) - Same OAuth feature in types
• runner.RunConfig.token_exchange_config (line 129) - Token exchange support

⚠️ Potential Issues

No Breaking Changes Detected

• All existing references to registry models appear compatible
• Code in toolhive_client.py, ingestion.py uses registry objects properly
• Models maintain backward compatibility

📋 Recommendations

1. Consider running validation: Since these are auto-generated models, verify they work with actual ToolHive API responses
2. Check for unused imports: With registry.py deleted, ensure no stale imports remain in other files
3. Test OAuth flows: New resource field and token exchange config should be tested with OAuth providers

Security Note

✅ No security concerns - these are data models with proper type validation via Pydantic

Summary

This is a clean refactoring of auto-generated code. The module split improves maintainability without introducing breaking changes. Approved for merge after validation testing.