feat: Azure OpenAI / Azure AI Foundry support. Models should be defined in conf/azure_models.json (or a custom path). See .env.example for environment variables or see readme. https://github.com/BeehiveInnovations/zen-mcp-server/issues/265
feat: OpenRouter / Custom Models / Azure can separately also use custom config paths now (see .env.example )
refactor: Model registry class made abstract, OpenRouter / Custom Provider / Azure OpenAI now subclass these
refactor: breaking change: `is_custom` property has been removed from model_capabilities.py (and thus custom_models.json) given each models are now read from separate configuration files
Add ZEN_MCP_FORCE_ENV_OVERRIDE configuration to control whether .env file values
override system environment variables. This prevents conflicts when multiple AI
tools pass different cached environment variables to the MCP server.
- Use dotenv_values() to read configuration from .env file only
- Apply conditional override based on configuration setting
- Add appropriate logging for transparency
- Update .env.example with detailed configuration documentation
- Maintains backward compatibility with default behavior (false)
- Add GEMINI_BASE_URL configuration option in .env.example
- Implement custom endpoint support in GeminiModelProvider using HttpOptions
- Update registry to pass base_url parameter to Gemini provider
- Maintain backward compatibility - uses default Google endpoint when not configured
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)
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
## Description
This PR adds support for selectively disabling tools via the DISABLED_TOOLS environment variable, allowing users to customize which MCP tools are available in their Zen server instance. This feature enables better control over tool availability for security, performance, or organizational requirements.
## Changes Made
- [x] Added `DISABLED_TOOLS` environment variable support to selectively disable tools
- [x] Implemented tool filtering logic with protection for essential tools (version, listmodels)
- [x] Added comprehensive validation with warnings for unknown tools and attempts to disable essential tools
- [x] Updated `.env.example` with DISABLED_TOOLS documentation and examples
- [x] Added comprehensive test suite (16 tests) covering all edge cases
- [x] No breaking changes - feature is opt-in with default behavior unchanged
## Configuration
Add to `.env` file:
```bash
# Optional: Tool Selection
# Comma-separated list of tools to disable. If not set, all tools are enabled.
# Essential tools (version, listmodels) cannot be disabled.
# Available tools: chat, thinkdeep, planner, consensus, codereview, precommit,
# debug, docgen, analyze, refactor, tracer, testgen
# Examples:
# DISABLED_TOOLS= # All tools enabled (default)
# DISABLED_TOOLS=debug,tracer # Disable debug and tracer tools
# DISABLED_TOOLS=planner,consensus # Disable planning tools
## 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)
Description: This feature adds support for UTF-8 encoding in JSON responses, allowing for proper handling of special characters and emojis.
- Implement unit tests for UTF-8 encoding in various model providers including Gemini, OpenAI, and OpenAI Compatible.
- Validate UTF-8 support in token counting, content generation, and error handling.
- Introduce tests for JSON serialization ensuring proper handling of French characters and emojis.
- Create tests for language instruction generation based on locale settings.
- Validate UTF-8 handling in workflow tools including AnalyzeTool, CodereviewTool, and DebugIssueTool.
- Ensure that all tests check for correct UTF-8 character preservation and proper JSON formatting.
- Add integration tests to verify the interaction between locale settings and model responses.
* Migration from docker to standalone server
Migration handling
Fixed tests
Use simpler in-memory storage
Support for concurrent logging to disk
Simplified direct connections to localhost
* Migration from docker / redis to standalone script
Updated tests
Updated run script
Fixed requirements
Use dotenv
Ask if user would like to install MCP in Claude Desktop once
Updated docs
* More cleanup and references to docker removed
* Cleanup
* Comments
* Fixed tests
* Fix GitHub Actions workflow for standalone Python architecture
- Install requirements-dev.txt for pytest and testing dependencies
- Remove Docker setup from simulation tests (now standalone)
- Simplify linting job to use requirements-dev.txt
- Update simulation tests to run directly without Docker
Fixes unit test failures in CI due to missing pytest dependency.
🤖 Generated with [Claude Code](https://claude.ai/code)
Co-Authored-By: Claude <noreply@anthropic.com>
* Remove simulation tests from GitHub Actions
- Removed simulation-tests job that makes real API calls
- Keep only unit tests (mocked, no API costs) and linting
- Simulation tests should be run manually with real API keys
- Reduces CI costs and complexity
GitHub Actions now only runs:
- Unit tests (569 tests, all mocked)
- Code quality checks (ruff, black)
🤖 Generated with [Claude Code](https://claude.ai/code)
Co-Authored-By: Claude <noreply@anthropic.com>
* Fixed tests
* Fixed tests
---------
Co-authored-by: Claude <noreply@anthropic.com>
New test to confirm history build-up and system prompt does not affect prompt size checks
Also check for large prompts in focus_on
Fixed .env.example incorrectly did not comment out CUSTOM_API causing the run-server script to think at least one key exists
* 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
## Major Features Added
### 🎯 Dynamic Configuration System
- **Environment-aware model selection**: DEFAULT_MODEL with 'pro'/'flash' shortcuts
- **Configurable thinking modes**: DEFAULT_THINKING_MODE_THINKDEEP for extended reasoning
- **All tool schemas now dynamic**: Show actual current defaults instead of hardcoded values
- **Enhanced setup workflow**: Copy from .env.example with smart customization
### 🔧 Model & Thinking Configuration
- **Smart model resolution**: Support both shortcuts ('pro', 'flash') and full model names
- **Thinking mode optimization**: Only apply thinking budget to models that support it
- **Flash model compatibility**: Works without thinking config, still beneficial via system prompts
- **Dynamic schema descriptions**: Tool parameters show current environment values
### 🚀 Enhanced Developer Experience
- **Fail-fast Docker setup**: GEMINI_API_KEY required upfront in docker-compose
- **Comprehensive startup logging**: Shows current model and thinking mode defaults
- **Enhanced get_version tool**: Reports all dynamic configuration values
- **Better .env documentation**: Clear token consumption details and model options
### 🧪 Comprehensive Testing
- **Live model validation**: New simulator test validates Pro vs Flash thinking behavior
- **Dynamic configuration tests**: Verify environment variable overrides work correctly
- **Complete test coverage**: All 139 unit tests pass, including new model config tests
### 📋 Configuration Files Updated
- **docker-compose.yml**: Fail-fast API key validation, thinking mode support
- **setup-docker.sh**: Copy from .env.example instead of manual creation
- **.env.example**: Detailed documentation with token consumption per thinking mode
- **.gitignore**: Added test-setup/ for cleanup
### 🛠 Technical Improvements
- **Removed setup.py**: Fully Docker-based deployment (no longer needed)
- **REDIS_URL smart defaults**: Auto-configured for Docker, still configurable for dev
- **All tools updated**: Consistent dynamic model parameter descriptions
- **Enhanced error handling**: Better model resolution and validation
## Breaking Changes
- Removed setup.py (Docker-only deployment)
- Model parameter descriptions now show actual defaults (dynamic)
## Migration Guide
- Update .env files using new .env.example format
- Use 'pro'/'flash' shortcuts or full model names
- Set DEFAULT_THINKING_MODE_THINKDEEP for custom thinking depth
🤖 Generated with [Claude Code](https://claude.ai/code)
Co-Authored-By: Claude <noreply@anthropic.com>
This commit addresses several critical issues and improvements:
🔧 Critical Fixes:
- Fixed conversation history not being included when using continuation_id in AI-to-AI conversations
- Fixed test mock targeting issues preventing proper conversation memory validation
- Fixed Docker debug logging functionality with Gemini tools
🐛 Bug Fixes:
- Docker compose configuration for proper container command execution
- Test mock import targeting from utils.conversation_memory.* to tools.base.*
- Version bump to 3.1.0 reflecting significant improvements
🚀 Improvements:
- Enhanced Docker environment configuration with comprehensive logging setup
- Added cross-tool continuation documentation and examples in README
- Improved error handling and validation across all tools
- Better logging configuration with LOG_LEVEL environment variable support
- Enhanced conversation memory system documentation
🧪 Testing:
- Added comprehensive conversation history bug fix tests
- Added cross-tool continuation functionality tests
- All 132 tests now pass with proper conversation history validation
- Improved test coverage for AI-to-AI conversation threading
✨ Code Quality:
- Applied black, isort, and ruff formatting across entire codebase
- Enhanced inline documentation for conversation memory system
- Cleaned up temporary files and improved repository hygiene
- Better test descriptions and coverage for critical functionality
🤖 Generated with [Claude Code](https://claude.ai/code)
Co-Authored-By: Claude <noreply@anthropic.com>
- Fixed review_changes tool to properly translate host paths to container paths in Docker
- Prevents "No such file or directory" errors when running in Docker containers
- Added proper error handling with clear messages when paths are inaccessible
refactor: Centralized token limit validation across all tools
- Added _validate_token_limit method to BaseTool to eliminate code duplication
- Reduced ~25 lines of duplicated code across 5 tools (analyze, chat, debug_issue, review_code, think_deeper)
- Maintains exact same error messages and behavior
feat: Enhanced large prompt handling
- Added support for prompts >50K chars by requesting file-based input
- Preserves MCP's ~25K token capacity for responses
- All tools now check prompt size before processing
test: Added comprehensive Docker path integration tests
- Tests for path translation, security validation, and error handling
- Tests for review_changes tool specifically with Docker paths
- Fixed failing think_deeper test (updated default from "max" to "high")
chore: Code quality improvements
- Applied black formatting across all files
- Fixed import sorting with isort
- All tests passing (96 tests)
- Standardized error handling follows MCP TextContent format
The changes ensure consistent behavior across all environments while reducing code duplication and improving maintainability.
🤖 Generated with [Claude Code](https://claude.ai/code)
Co-Authored-By: Claude <noreply@anthropic.com>
Implements comprehensive Docker support to eliminate Python version and dependency concerns.
Users can now run the MCP server in a container with automatic path translation between
host and container filesystems.
Key features:
- Dockerfile with multi-architecture support (amd64/arm64)
- Automatic path translation using WORKSPACE_ROOT environment variable
- Setup scripts for all platforms (Bash, CMD, PowerShell)
- GitHub Actions workflow for automated Docker Hub publishing
- Secure non-root container execution
- Read-only volume mounts by default
The setup process is now simplified to:
1. Run setup-docker-env script to generate .env and Claude config
2. Build the Docker image
3. Copy generated config to Claude Desktop
No Python installation or virtual environment management required.
Fixes#3
Co-Authored-By: Claude <noreply@anthropic.com>
