- Add verification that o3-pro model was actually used (not just requested)
- Verify model_used and provider_used metadata fields are populated
- Add graceful handling for error responses in test
- Improve test documentation explaining what's being verified
- Confirm response parsing uses output_text field correctly
This ensures the test properly validates both that:
1. The o3-pro model was selected and used via the /v1/responses endpoint
2. The response metadata correctly identifies the model and provider
🤖 Generated with [Claude Code](https://claude.ai/code)
Co-Authored-By: Claude <noreply@anthropic.com>
- Fix TOCTOU race condition by removing os.path.exists() check before file open
- Move imports (base64, binascii, os, utils.file_types) to top of file
- Replace broad Exception catch with specific binascii.Error for base64 decoding
- Maintain proper error handling and test compatibility
Consolidates duplicated image validation logic from individual providers
into a reusable base class method. This improves maintainability and
ensures consistent validation across all providers.
- Added validate_image() method to ModelProvider base class
- Supports both file paths and data URLs
- Validates image format, size, and MIME types
- Added DEFAULT_MAX_IMAGE_SIZE_MB class constant (20MB)
- Refactored Gemini and OpenAI providers to use base validation
- Added comprehensive test suite with 19 tests
- Used minimal mocking approach with concrete test provider class
Modified run-server.sh to allow developers to set up the Python development
environment without having API keys configured. This enables:
- Developers to clone and immediately start working on the codebase
- Running tests that don't require API calls
- Browsing and understanding the code structure
- Adding API keys later when ready to test MCP server functionality
Changes:
- Added new check_api_keys() function that warns but doesn't exit
- Changed print_error to print_warning for missing keys
- Updated main() to use check_api_keys instead of validate_api_keys || exit 1
- Kept original validate_api_keys() for backward compatibility
The script now shows a warning when API keys are missing but continues
with the full Python environment setup, dependencies installation, and
Claude configuration.
- Fix lint errors: trailing whitespace and deprecated typing imports
- Update test mock for o3-pro response format (output.content[] → output_text)
- Implement robust test isolation with monkeypatch fixture
- Clear provider registry cache to prevent test interference
- Ensure o3-pro tests pass in both individual and full suite execution
🤖 Generated with [Claude Code](https://claude.ai/code)
Co-Authored-By: Claude <noreply@anthropic.com>
- Add o3_pro_basic_math.json cassette for test_o3_pro_output_text_fix.py
- Remove unused o3_pro_content_capture.json cassette
- This allows tests to run without API keys in CI/CD
🤖 Generated with [Claude Code](https://claude.ai/code)
Co-Authored-By: Claude <noreply@anthropic.com>
- Fix o3-pro response parsing to use output_text convenience field
- Replace respx with custom httpx transport solution for better reliability
- Implement comprehensive PII sanitization to prevent secret exposure
- Add HTTP request/response recording with cassette format for testing
- Sanitize all existing cassettes to remove exposed API keys
- Update documentation to reflect new HTTP transport recorder
- Add test suite for PII sanitization and HTTP recording
This change:
1. Fixes timeout issues with o3-pro API calls (was 2+ minutes, now ~15-22 seconds)
2. Properly captures response content without httpx.ResponseNotRead exceptions
3. Preserves original HTTP response format including gzip compression
4. Prevents future secret exposure with automatic PII sanitization
5. Enables reliable replay testing for o3-pro interactions
Co-Authored-By: Claude <noreply@anthropic.com>
Add the Docker parameter to build the Zen MCP server Docker imag
Add environnement detection (Python vs Docker) to configure the MCP server command in MCP client settings
Add the Docker parameter to build the Zen MCP server Docker imag
Add environnement detection (Python vs Docker) to configure the MCP server command in MCP client settings
- Improved error handling and path resolution in run-server.ps1 for better reliability.
- Implemented conversation tests for Docker mode compatibility in validation_crossplatform.py.
- Updated run-server.ps1 to include detailed help documentation, configuration management, and backup retention for configuration files.
- Added Docker path validation tests in validation_crossplatform.py to ensure correct path handling in Docker mode.
- Enhanced integration test script run_integration_tests.ps1 with comprehensive documentation and parameter support for output customization.
- Introduced tests for Docker deployment scripts to ensure existence, permissions, and proper command usage.
- Added tests for Docker integration with Claude Desktop, validating MCP configuration and command formats.
- Implemented health check tests for Docker, ensuring script functionality and proper configuration in Docker setup.
- Created tests for Docker MCP validation, focusing on command validation and security configurations.
- Developed security tests for Docker configurations, checking for non-root user setups, privilege restrictions, and sensitive data handling.
- Added volume persistence tests to ensure configuration and logs are correctly managed across container runs.
- Updated .dockerignore to exclude sensitive files and added relevant tests for Docker secrets handling.
Added new confidence values (very_high, almost_certain) to all workflow tools
to provide more granular confidence tracking. Updated enum declarations in:
- analyze.py, codereview.py, debug.py, precommit.py, secaudit.py, testgen.py
- Updated debug.py's get_required_actions to handle new confidence values
- All tools now use consistent 7-value confidence scale
- refactor.py kept its unique scale (exploring/incomplete/partial/complete)
Also fixed model thinking configuration:
- Added very_high and almost_certain to MODEL_THINKING_PREFERENCES
- Set medium thinking for very_high, high thinking for almost_certain
- Updated prompts to clarify certain means 100% local confidence
🤖 Generated with [Claude Code](https://claude.ai/code)
Co-Authored-By: Claude <noreply@anthropic.com>
- Implemented `run-server.ps1` for setting up the Zen MCP server environment, including virtual environment creation, dependency installation, and Docker cleanup.
- Added logging and error handling throughout the setup process.
- Included functions for validating API keys and configuring integration with Claude Desktop and Gemini CLI.
- Created `run_integration_tests.ps1` to execute integration tests with real API calls, including checks for API key availability and environment setup.
- Enhanced output with color-coded messages for better user experience.
Patch directory added for cross-platform patching support (`patch_crossplatform.py`).
