Stay in codex, plan review and fix complicated bugs, then ask it to spawn claude code and implement the plan.
This uses your current subscription instead of API tokens.
model definitions now support a new `allow_code_generation` flag, only to be used with higher reasoning models such as GPT-5-Pro and-Gemini 2.5-Pro
When `true`, the `chat` tool can now request the external model to generate a full implementation / update / instructions etc and then share the implementation with the calling agent.
This effectively allows us to utilize more powerful models such as GPT-5-Pro to generate code for us or entire implementations (which are either API-only or part of the $200 Pro plan from within the ChatGPT app)
Zen now allows you to define `roles` for an external CLI and delegate work to another CLI via the new `clink` tool (short for `CLI + Link`). Gemini, for instance, offers 1000 free requests a day - this means you can save on tokens and your weekly limits within Claude Code by delegating work to another entirely capable CLI agent!
Define your own system prompts as `roles` and make another CLI do anything you'd like. Like the current tool you're connected to, the other CLI has complete access to your files and the current context. This also works incredibly well with Zen's `conversation continuity`.
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
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
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)
README improvements:
- Reduce README from 725 to 169 lines (77% reduction)
- Focus on quick start and essential information
- Link to detailed docs instead of duplicating content
- Improve scannability with clear sections and emojis
- Add concise tool categorization and workflows
Documentation structure:
- Create comprehensive getting-started.md guide
- Move detailed setup instructions from README
- Include troubleshooting, configuration templates
- Add step-by-step installation for all methods
Benefits:
- Faster onboarding for new users
- Progressive disclosure of information
- Better GitHub discovery experience
- Maintainable documentation structure
- Clear separation of concerns
The README now serves as an effective landing page while the
detailed documentation provides comprehensive guidance.
## 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)
