fix: model definition re-introduced into the schema but intelligently and only a summary is generated per tool. Required to ensure CLI calls and uses the correct model
fix: removed `model` param from some tools where this wasn't needed
fix: fixed adherence to `*_ALLOWED_MODELS` by advertising only the allowed models to the CLI
fix: removed duplicates across providers when passing canonical names back to the CLI; the first enabled provider wins
docs: document provider base class
refactor: cleanup custom provider, it should only deal with `is_custom` model configurations
fix: make sure openrouter provider does not load `is_custom` models
fix: listmodels tool cleanup
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
- 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
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
## Description
This PR implements a new [DIAL](https://dialx.ai/dial_api) (Data & AI Layer) provider for the Zen MCP Server, enabling unified access to multiple AI models through the DIAL API platform. DIAL provides enterprise-grade AI model access with deployment-specific routing similar to Azure OpenAI.
## Changes Made
- [x] Added support of atexit:
- Ensures automatic cleanup of provider resources (HTTP clients, connection pools) on server shutdown
- Fixed bug using ModelProviderRegistry.get_available_providers() instead of accessing private _providers
- Works with SIGTERM/Ctrl+C for graceful shutdown in both development and containerized environments
- [x] Added new DIAL provider (`providers/dial.py`) inheriting from `OpenAICompatibleProvider`
- [x] Updated server.py to register DIAL provider during initialization
- [x] Updated provider registry to include DIAL provider type
- [x] Implemented deployment-specific routing for DIAL's Azure OpenAI-style endpoints
- [x] Implemented performance optimizations:
- Connection pooling with httpx for better performance
- Thread-safe client caching with double-check locking pattern
- Proper resource cleanup with `close()` method
- [x] Added comprehensive unit tests with 16 test cases (`tests/test_dial_provider.py`)
- [x] Added DIAL configuration to `.env.example` with documentation
- [x] Added support for configurable API version via `DIAL_API_VERSION` environment variable
- [x] Added DIAL model restrictions support via `DIAL_ALLOWED_MODELS` environment variable
### Supported DIAL Models:
- OpenAI models: o3, o4-mini (and their dated versions)
- Google models: gemini-2.5-pro, gemini-2.5-flash (including search variant)
- Anthropic models: Claude 4 Opus/Sonnet (with and without thinking mode)
### Environment Variables:
- `DIAL_API_KEY`: Required API key for DIAL authentication
- `DIAL_API_HOST`: Optional base URL (defaults to https://core.dialx.ai)
- `DIAL_API_VERSION`: Optional API version header (defaults to 2025-01-01-preview)
- `DIAL_ALLOWED_MODELS`: Optional comma-separated list of allowed models
### Breaking Changes:
- None
### Dependencies:
- No new dependencies added (uses existing OpenAI SDK with custom routing)
* WIP: new workflow architecture
* WIP: further improvements and cleanup
* WIP: cleanup and docks, replace old tool with new
* WIP: cleanup and docks, replace old tool with new
* WIP: new planner implementation using workflow
* WIP: precommit tool working as a workflow instead of a basic tool
Support for passing False to use_assistant_model to skip external models completely and use Claude only
* WIP: precommit workflow version swapped with old
* WIP: codereview
* WIP: replaced codereview
* WIP: replaced codereview
* WIP: replaced refactor
* WIP: workflow for thinkdeep
* WIP: ensure files get embedded correctly
* WIP: thinkdeep replaced with workflow version
* WIP: improved messaging when an external model's response is received
* WIP: analyze tool swapped
* WIP: updated tests
* Extract only the content when building history
* Use "relevant_files" for workflow tools only
* WIP: updated tests
* Extract only the content when building history
* Use "relevant_files" for workflow tools only
* WIP: fixed get_completion_next_steps_message missing param
* Fixed tests
Request for files consistently
* Fixed tests
Request for files consistently
* Fixed tests
* New testgen workflow tool
Updated docs
* Swap testgen workflow
* Fix CI test failures by excluding API-dependent tests
- Update GitHub Actions workflow to exclude simulation tests that require API keys
- Fix collaboration tests to properly mock workflow tool expert analysis calls
- Update test assertions to handle new workflow tool response format
- Ensure unit tests run without external API dependencies in CI
🤖 Generated with [Claude Code](https://claude.ai/code)
Co-Authored-By: Claude <noreply@anthropic.com>
* WIP - Update tests to match new tools
* WIP - Update tests to match new tools
* WIP - Update tests to match new tools
* Should help with https://github.com/BeehiveInnovations/zen-mcp-server/issues/97
Clear python cache when running script: https://github.com/BeehiveInnovations/zen-mcp-server/issues/96
Improved retry error logging
Cleanup
* WIP - chat tool using new architecture and improved code sharing
* Removed todo
* Removed todo
* Cleanup old name
* Tweak wordings
* Tweak wordings
Migrate old tests
* Support for Flash 2.0 and Flash Lite 2.0
* Support for Flash 2.0 and Flash Lite 2.0
* Support for Flash 2.0 and Flash Lite 2.0
Fixed test
* Improved consensus to use the workflow base class
* Improved consensus to use the workflow base class
* Allow images
* Allow images
* Replaced old consensus tool
* Cleanup tests
* Tests for prompt size
* New tool: docgen
Tests for prompt size
Fixes: https://github.com/BeehiveInnovations/zen-mcp-server/issues/107
Use available token size limits: https://github.com/BeehiveInnovations/zen-mcp-server/issues/105
* Improved docgen prompt
Exclude TestGen from pytest inclusion
* Updated errors
* Lint
* DocGen instructed not to fix bugs, surface them and stick to d
* WIP
* Stop claude from being lazy and only documenting a small handful
* More style rules
---------
Co-authored-by: Claude <noreply@anthropic.com>
Fix for: https://github.com/BeehiveInnovations/zen-mcp-server/issues/102
- Removed centralized MODEL_CAPABILITIES_DESC from config.py
- Added model descriptions to individual provider SUPPORTED_MODELS
- Updated _get_available_models() to use ModelProviderRegistry for API key filtering
- Added comprehensive test suite validating bug reproduction and fix
* Support for Custom URLs and custom models, including locally hosted models such as ollama
* Support for native + openrouter + local models (i.e. dozens of models) means you can start delegating sub-tasks to particular models or work to local models such as localizations or other boring work etc.
* Several tests added
* precommit to also include untracked (new) files
* Logfile auto rollover
* Improved logging
Simulation tests to confirm threading and history traversal
Chain of communication and branching validation tests from live simulation
Temperature enforcement per model
