Fixed issue where OpenAI models appeared twice in listmodels output by:
- Removing self-referencing aliases from OpenAI model definitions (e.g., "gpt-5" no longer includes "gpt-5" in its aliases)
- Adding filter in listmodels.py to skip aliases that match the model name
- Cleaned up inconsistent alias naming (o3-pro -> o3pro)
This ensures each model appears only once in the listing while preserving all useful aliases.
🤖 Generated with [Claude Code](https://claude.ai/code)
Co-Authored-By: Claude <noreply@anthropic.com>
Address code review feedback by creating ModelContext instance once
at the beginning of _consult_model method instead of creating it twice.
- Move ModelContext import to method beginning for better practice
- Create single ModelContext instance and reuse for both file processing
and temperature validation
- Remove redundant ModelContext creation on line 558
- Improve code clarity and efficiency as suggested by code review
Fixed runtime bug where _prepare_file_content_for_prompt was called
without required model_context parameter, causing RuntimeError when
processing requests with relevant_files.
- Create ModelContext instance with model_name in _consult_model method
- Pass model_context parameter to _prepare_file_content_for_prompt call
- Add comprehensive regression test to prevent future occurrences
- Maintain consensus tool's blinded design with independent model contexts
- Fix ModelContext constructor call in consensus tool (remove invalid parameters)
- Refactor temperature pattern matching for better readability per code review
- All tests now passing (799/799 passed)
- Fix consensus tool hardcoded temperature=0.2 bypassing model capabilities
- Add intelligent temperature inference for unknown custom models
- Support multi-model collaboration (O3, Gemini, Claude, Mistral, DeepSeek)
- Only OpenAI O-series and DeepSeek reasoner models reject temperature
- Most reasoning models (Gemini Pro, Claude, Mistral) DO support temperature
- Comprehensive logging for temperature decisions and user guidance
Resolves: https://github.com/BeehiveInnovations/zen-mcp-server/issues/245
Disabled secondary tools by default (for new installations), updated README.md with instructions on how to enable these in .env
run-server.sh now displays disabled / enabled tools (when DISABLED_TOOLS is set)
fix: Minor tweaks to prompts
fix: Improved support for smaller models that struggle with strict structured JSON output
Rearranged reasons to use the MCP above quick start (collapsed)
This commit updates all references to Claude Opus 4 and Sonnet 4 to their newer 4.1 versions throughout the codebase.
The changes include:
- Updating model names in `conf/custom_models.json` and `providers/dial.py`.
- Updating aliases and descriptions to match the new model versions.
- Updating `.env.example` to reflect the new model names.
- Updating all relevant test suites to use the new model names and ensure all tests pass.
Improvements to model name resolution
Improved instructions for multi-step workflows when continuation is available
Improved instructions for chat tool
Improved preferred model resolution, moved code from registry -> each provider
Updated tests
- Update arguments parameter type hint from Optional[dict] to Optional[dict[str, Any]] in workflow_mixin.py
- Ensures consistency with BaseTool and improves static analysis and code clarity
- No functional changes, only type annotation improvement
Co-authored-by: gemini-code-assist[bot] <176961590+gemini-code-assist[bot]@users.noreply.github.com>
- Add LOCALE-based localization support to all workflow tools
- Harmonize method signatures for prepare_prompt and _prepare_file_content_for_prompt
- Remove obsolete methods and clarify comments
- Ensure consistent behavior between SimpleTool and WorkflowTool
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>
Moved aliases as part of SUPPORTED_MODELS instead of shorthand, more in line with how custom_models are declared
Further refactoring to cleanup some code
