- Move dotenv_values import to top level with load_dotenv
- Fix logging sequence issue by deferring ZEN_MCP_FORCE_ENV_OVERRIDE logs until after logger configuration
- Apply Black formatting to ensure consistent code style
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)
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)
* fix: remove duplicate version tool registration
The version tool was appearing twice in the MCP tool list due to:
- VersionTool class properly registered in TOOLS dictionary (line 181)
- Hardcoded Tool() registration in handle_list_tools() (lines 451-462)
This duplicate was leftover from the architectural migration:
- June 8, 2025: Original hardcoded "get_version" tool added
- June 14, 2025: Renamed from "get_version" to "version"
- June 21, 2025: VersionTool class added during workflow architecture migration
- The old hardcoded registration was never removed
The hardcoded registration has been removed since VersionTool provides
identical functionality through the proper architecture.
Fixes: BeehiveInnovations/zen-mcp-server#120
* fix: complete removal of legacy version tool code
Following up on the duplicate version tool fix, this commit removes
all remaining dead code identified by Gemini Code Assist:
- Removed dead elif block for version tool (lines 639-643)
This block was unreachable since version is handled by TOOLS registry
- Removed orphaned handle_version() function (lines 942-1030)
No longer called after elif block removal
- Fixed imports: removed unused __author__ and __updated__ imports
These were remnants from the June 2025 migration from function-based
to class-based tools. The VersionTool class now handles all version
functionality through the standard tool architecture.
All 546 tests pass - no functional changes.
Related to: BeehiveInnovations/zen-mcp-server#120
* Fix model metadata preservation when using continuation_id
When continuing a conversation without specifying a model, the system now
correctly retrieves and uses the model from the previous assistant turn
instead of defaulting to DEFAULT_MODEL. This ensures model continuity
across conversation turns and fixes the metadata mismatch issue.
The fix:
- In reconstruct_thread_context(), check for previous assistant turns
- If no model is specified in the continuation request, use the model
from the most recent assistant turn
- This preserves the model choice across conversation continuations
Added comprehensive tests to verify the fix handles:
- Single turn conversations
- Multiple turns with different models
- No previous assistant turns (falls back to DEFAULT_MODEL)
- Explicit model specification (overrides previous turn)
- Thread chain relationships
Fixes issue where continuation metadata would incorrectly report
'llama3.2' instead of the actual model used (e.g., 'deepseek-r1-8b')
* Update test to reference issue #111
* Refactor tests to call reconstruct_thread_context directly
Address Gemini Code Assist feedback by removing duplicated implementation
logic from tests. Tests now call the actual function with proper mocking
instead of reimplementing the model retrieval logic.
This improves maintainability and ensures tests validate actual behavior
rather than their own copy of the logic.
* 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>
* 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
---------
Co-authored-by: Claude <noreply@anthropic.com>
* 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>
* WIP
Refactor resolving mode_names, should be done once at MCP call boundary
Pass around model context instead
Consensus tool allows one to get a consensus from multiple models, optionally assigning one a 'for' or 'against' stance to find nuanced responses.
* Deduplication of model resolution, model_context should be available before reaching deeper parts of the code
Improved abstraction when building conversations
Throw programmer errors early
* Guardrails
Support for `model:option` format at MCP boundary so future tools can use additional options if needed instead of handling this only for consensus
Model name now supports an optional ":option" for future use
* Simplified async flow
* Improved model for request to support natural language
Simplified async flow
* Improved model for request to support natural language
Simplified async flow
* Fix consensus tool async/sync patterns to match codebase standards
CRITICAL FIXES:
- Converted _get_consensus_responses from async to sync (matches other tools)
- Converted store_conversation_turn from async to sync (add_turn is synchronous)
- Removed unnecessary asyncio imports and sleep calls
- Fixed ClosedResourceError in MCP protocol during long consensus operations
PATTERN ALIGNMENT:
- Consensus tool now follows same sync patterns as all other tools
- Only execute() and prepare_prompt() are async (base class requirement)
- All internal operations are synchronous like analyze, chat, debug, etc.
TESTING:
- MCP simulation test now passes: consensus_stance ✅
- Two-model consensus works correctly in ~35 seconds
- Unknown stance handling defaults to neutral with warnings
- All 9 unit tests pass (100% success rate)
The consensus tool async patterns were anomalous in the codebase.
This fix aligns it with the established synchronous patterns used
by all other tools while maintaining full functionality.
🤖 Generated with [Claude Code](https://claude.ai/code)
Co-Authored-By: Claude <noreply@anthropic.com>
* Fixed call order and added new test
* Cleanup dead comments
Docs for the new tool
Improved tests
---------
Co-authored-by: Claude <noreply@anthropic.com>
New tool to list all models `listmodels`
Integration test to for all the different combinations of API keys
Tweaks to codereview prompt for a better quality input from Claude
Fixed missing 'low' severity in codereview
Fixed MagicMock comparison errors across multiple test suites by:
- Adding proper ModelCapabilities mocks with real values instead of MagicMock objects
- Updating test_auto_mode.py with correct provider mocking for model availability tests
- Updating test_thinking_modes.py with proper capabilities mocking in all thinking mode tests
- Updating test_tools.py with proper capabilities mocking for CodeReview and Analyze tools
- Fixing test_large_prompt_handling.py by adding proper provider mocking to prevent errors before large prompt detection
Fixed pytest collection warnings by:
- Renaming TestGenRequest to TestGenerationRequest to avoid pytest collecting it as a test class
- Renaming TestGenTool to TestGenerationTool to avoid pytest collecting it as a test class
- Updated all imports and references across server.py, tools/__init__.py, and test files
All 459 tests now pass without warnings or MagicMock comparison errors.
🤖 Generated with [Claude Code](https://claude.ai/code)
Co-Authored-By: Claude <noreply@anthropic.com>
Docs added to show how a new tool is created
All tools should add numbers to code for models to be able to reference if needed
Enabled line numbering for code for all tools to use
Additional tests to validate line numbering is not added to git diffs
Supports decomposing large components and files, finding codesmells, finding modernizing opportunities as well as code organization opportunities. Fix this mega-classes today!
Line numbers added to embedded code for better references from model -> claude
Improved testgen; encourages follow-ups with less work in between and less token generation to avoid surpassing the 25K barrier
Improved coderevew tool to request a focused code review instead where a single-pass code review is too large or complex
* 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
