EX MCP Server

# Adding a New Provider This guide explains how to add support for a new AI model provider to the EXAI MCP Server. The provider system is designed to be extensible and follows a simple pattern. ## Overview Each provider: - Inherits from `ModelProvider` (base class) or `OpenAICompatibleProvider` (for OpenAI-compatible APIs) - Defines supported models using `ModelCapabilities` objects - Implements a few core abstract methods - Gets registered automatically via environment variables ## Choose Your Implementation Path **Option A: Full Provider (`ModelProvider`)** - For APIs with unique features or custom authentication - Complete control over API calls and response handling - Required methods: `generate_content()`, `count_tokens()`, `get_capabilities()`, `validate_model_name()`, `supports_thinking_mode()`, `get_provider_type()` **Option B: OpenAI-Compatible (`OpenAICompatibleProvider`)** - For APIs that follow OpenAI's chat completion format - Only need to define: model configurations, capabilities, and validation - Inherits all API handling automatically ⚠️ **Important**: If using aliases (like `"gpt"` → `"gpt-4"`), override `generate_content()` to resolve them before API calls. ## Step-by-Step Guide ### 1. Add Provider Type Add your provider to `ProviderType` enum in `src/providers/base.py`: ```python class ProviderType(Enum): KIMI = "kimi" GLM = "glm" EXAMPLE = "example" # Add this ``` ### 2. Create the Provider Implementation #### Option A: Full Provider (Native Implementation) Create `src/providers/example.py`: ```python """Example model provider implementation.""" import logging from typing import Optional from .base import ModelCapabilities, ModelProvider, ModelResponse, ProviderType, RangeTemperatureConstraint logger = logging.getLogger(__name__) class ExampleModelProvider(ModelProvider): """Example model provider implementation.""" # Define models using ModelCapabilities objects (see Kimi/GLM providers) SUPPORTED_MODELS = { "example-large": ModelCapabilities( provider=ProviderType.EXAMPLE, model_name="example-large", friendly_name="Example Large", context_window=100_000, max_output_tokens=50_000, supports_extended_thinking=False, temperature_constraint=RangeTemperatureConstraint(0.0, 2.0, 0.7), description="Large model for complex tasks", aliases=["large", "big"], ), "example-small": ModelCapabilities( provider=ProviderType.EXAMPLE, model_name="example-small", friendly_name="Example Small", context_window=32_000, max_output_tokens=16_000, temperature_constraint=RangeTemperatureConstraint(0.0, 2.0, 0.7), description="Fast model for simple tasks", aliases=["small", "fast"], ), } def __init__(self, api_key: str, **kwargs): super().__init__(api_key, **kwargs) # Initialize your API client here def get_capabilities(self, model_name: str) -> ModelCapabilities: resolved_name = self._resolve_model_name(model_name) if resolved_name not in self.SUPPORTED_MODELS: raise ValueError(f"Unsupported model: {model_name}") # Apply restrictions if needed from utils.model_restrictions import get_restriction_service restriction_service = get_restriction_service() if not restriction_service.is_allowed(ProviderType.EXAMPLE, resolved_name, model_name): raise ValueError(f"Model '{model_name}' is not allowed.") return self.SUPPORTED_MODELS[resolved_name] def generate_content(self, prompt: str, model_name: str, system_prompt: Optional[str] = None, temperature: float = 0.7, max_output_tokens: Optional[int] = None, **kwargs) -> ModelResponse: resolved_name = self._resolve_model_name(model_name) # Your API call logic here # response = your_api_client.generate(...) return ModelResponse( content="Generated response", # From your API usage={"input_tokens": 100, "output_tokens": 50, "total_tokens": 150}, model_name=resolved_name, friendly_name="Example", provider=ProviderType.EXAMPLE, ) def count_tokens(self, text: str, model_name: str) -> int: return len(text) // 4 # Simple estimation def get_provider_type(self) -> ProviderType: return ProviderType.EXAMPLE def validate_model_name(self, model_name: str) -> bool: resolved_name = self._resolve_model_name(model_name) return resolved_name in self.SUPPORTED_MODELS def supports_thinking_mode(self, model_name: str) -> bool: capabilities = self.get_capabilities(model_name) return capabilities.supports_extended_thinking ``` #### Option B: OpenAI-Compatible Provider (Simplified) For OpenAI-compatible APIs: ```python """Example OpenAI-compatible provider.""" from typing import Optional from .base import ModelCapabilities, ModelResponse, ProviderType, RangeTemperatureConstraint from .openai_compatible import OpenAICompatibleProvider class ExampleProvider(OpenAICompatibleProvider): """Example OpenAI-compatible provider.""" FRIENDLY_NAME = "Example" # Define models using ModelCapabilities (consistent with other providers) SUPPORTED_MODELS = { "example-model-large": ModelCapabilities( provider=ProviderType.EXAMPLE, model_name="example-model-large", friendly_name="Example Large", context_window=128_000, max_output_tokens=64_000, temperature_constraint=RangeTemperatureConstraint(0.0, 2.0, 0.7), aliases=["large", "big"], ), } def __init__(self, api_key: str, **kwargs): kwargs.setdefault("base_url", "https://api.example.com/v1") super().__init__(api_key, **kwargs) def get_capabilities(self, model_name: str) -> ModelCapabilities: resolved_name = self._resolve_model_name(model_name) if resolved_name not in self.SUPPORTED_MODELS: raise ValueError(f"Unsupported model: {model_name}") return self.SUPPORTED_MODELS[resolved_name] def get_provider_type(self) -> ProviderType: return ProviderType.EXAMPLE def validate_model_name(self, model_name: str) -> bool: resolved_name = self._resolve_model_name(model_name) return resolved_name in self.SUPPORTED_MODELS def generate_content(self, prompt: str, model_name: str, **kwargs) -> ModelResponse: # IMPORTANT: Resolve aliases before API call resolved_model_name = self._resolve_model_name(model_name) return super().generate_content(prompt=prompt, model_name=resolved_model_name, **kwargs) ``` ### 3. Register Your Provider Add environment variable mapping in `src/providers/registry.py`: ```python # In _get_api_key_for_provider method (src/providers/registry.py): key_mapping = { ProviderType.KIMI: "KIMI_API_KEY", ProviderType.GLM: "GLM_API_KEY", ProviderType.EXAMPLE: "EXAMPLE_API_KEY", # Add this } ``` Add to `server.py`: 1. **Import your provider**: ```python from src.providers.example import ExampleModelProvider ``` 2. **Add to `configure_providers()` function**: ```python # Check for Example API key example_key = os.getenv("EXAMPLE_API_KEY") if example_key: ModelProviderRegistry.register_provider(ProviderType.EXAMPLE, ExampleModelProvider) logger.info("Example API key found - Example models available") ``` 3. **Add to provider priority** (in `src/providers/registry.py`): ```python PROVIDER_PRIORITY_ORDER = [ ProviderType.KIMI, ProviderType.GLM, ProviderType.EXAMPLE, # Add your provider here ProviderType.CUSTOM, # Local models ProviderType.OPENROUTER, # Catch-all (keep last) ] ``` ### 4. Environment Configuration Add to your `.env` file: ```bash # Your provider's API key EXAMPLE_API_KEY=your_api_key_here # Optional: Disable specific tools DISABLED_TOOLS=debug,tracer ``` **Note**: The `description` field in `ModelCapabilities` helps Claude choose the best model in auto mode. ### 5. Test Your Provider Create basic tests to verify your implementation: ```python # Test model validation provider = ExampleModelProvider("test-key") assert provider.validate_model_name("large") == True assert provider.validate_model_name("unknown") == False # Test capabilities caps = provider.get_capabilities("large") assert caps.context_window > 0 assert caps.provider == ProviderType.EXAMPLE ``` ## Key Concepts ### Provider Priority When a user requests a model, providers are checked in priority order: 1. **Native providers** (Kimi, GLM, Example) - handle their specific models 2. **Custom provider** - handles local/self-hosted models 3. **OpenRouter** - catch-all for everything else ### Model Validation Your `validate_model_name()` should **only** return `True` for models you explicitly support: ```python def validate_model_name(self, model_name: str) -> bool: resolved_name = self._resolve_model_name(model_name) return resolved_name in self.SUPPORTED_MODELS # Be specific! ``` ### Model Aliases The base class handles alias resolution automatically via the `aliases` field in `ModelCapabilities`. ## Important Notes ### Alias Resolution in OpenAI-Compatible Providers If using `OpenAICompatibleProvider` with aliases, **you must override `generate_content()`** to resolve aliases before API calls: ```python def generate_content(self, prompt: str, model_name: str, **kwargs) -> ModelResponse: # Resolve alias before API call resolved_model_name = self._resolve_model_name(model_name) return super().generate_content(prompt=prompt, model_name=resolved_model_name, **kwargs) ``` Without this, API calls with aliases like `"large"` will fail because your API doesn't recognize the alias. ## Best Practices - **Be specific in model validation** - only accept models you actually support - **Use ModelCapabilities objects** consistently (see Kimi/GLM providers) - **Include descriptive aliases** for better user experience - **Add error handling** and logging for debugging - **Test with real API calls** to verify everything works - **Follow the existing patterns** in `src/providers/openai_compatible.py` and `src/providers/custom.py` ## Quick Checklist - [ ] Added to `ProviderType` enum in `src/providers/base.py` - [ ] Created provider class with all required methods - [ ] Added API key mapping in `src/providers/registry.py` - [ ] Added to provider priority order in `src/providers/registry.py` - [ ] Imported and registered in `server.py` - [ ] Basic tests verify model validation and capabilities - [ ] Tested with real API calls ## Examples See existing implementations: - **Full provider**: `src/providers/kimi.py` and `src/providers/glm.py` - **OpenAI-compatible**: `src/providers/custom.py` - **Base classes**: `src/providers/base.py` The modern approach uses `ModelCapabilities` objects directly in `SUPPORTED_MODELS`, making the implementation much cleaner and more consistent.