Browser-Use MCP Server

from typing import Any, Dict, List, Optional, Union from pydantic import Field, SecretStr, field_validator, ValidationInfo from pydantic_settings import BaseSettings, SettingsConfigDict class LLMSettings(BaseSettings): model_config = SettingsConfigDict(env_prefix="MCP_LLM_") provider: str = Field(default="google", env="PROVIDER") model_name: str = Field(default="gemini-2.5-flash-preview-04-17", env="MODEL_NAME") temperature: float = Field(default=0.0, env="TEMPERATURE") base_url: Optional[str] = Field(default=None, env="BASE_URL") api_key: Optional[SecretStr] = Field(default=None, env="API_KEY") # Generic API key # Provider-specific API keys openai_api_key: Optional[SecretStr] = Field(default=None, env="OPENAI_API_KEY") anthropic_api_key: Optional[SecretStr] = Field(default=None, env="ANTHROPIC_API_KEY") google_api_key: Optional[SecretStr] = Field(default=None, env="GOOGLE_API_KEY") azure_openai_api_key: Optional[SecretStr] = Field(default=None, env="AZURE_OPENAI_API_KEY") deepseek_api_key: Optional[SecretStr] = Field(default=None, env="DEEPSEEK_API_KEY") mistral_api_key: Optional[SecretStr] = Field(default=None, env="MISTRAL_API_KEY") openrouter_api_key: Optional[SecretStr] = Field(default=None, env="OPENROUTER_API_KEY") alibaba_api_key: Optional[SecretStr] = Field(default=None, env="ALIBABA_API_KEY") moonshot_api_key: Optional[SecretStr] = Field(default=None, env="MOONSHOT_API_KEY") unbound_api_key: Optional[SecretStr] = Field(default=None, env="UNBOUND_API_KEY") # Provider-specific endpoints openai_endpoint: Optional[str] = Field(default=None, env="OPENAI_ENDPOINT") anthropic_endpoint: Optional[str] = Field(default=None, env="ANTHROPIC_ENDPOINT") azure_openai_endpoint: Optional[str] = Field(default=None, env="AZURE_OPENAI_ENDPOINT") azure_openai_api_version: str = Field(default="2025-01-01-preview", env="AZURE_OPENAI_API_VERSION") deepseek_endpoint: Optional[str] = Field(default=None, env="DEEPSEEK_ENDPOINT") mistral_endpoint: Optional[str] = Field(default=None, env="MISTRAL_ENDPOINT") ollama_endpoint: str = Field(default="http://localhost:11434", env="OLLAMA_ENDPOINT") openrouter_endpoint: str = Field(default="https://openrouter.ai/api/v1", env="OPENROUTER_ENDPOINT") alibaba_endpoint: Optional[str] = Field(default=None, env="ALIBABA_ENDPOINT") moonshot_endpoint: Optional[str] = Field(default=None, env="MOONSHOT_ENDPOINT") unbound_endpoint: Optional[str] = Field(default=None, env="UNBOUND_ENDPOINT") ollama_num_ctx: Optional[int] = Field(default=32000, env="OLLAMA_NUM_CTX") ollama_num_predict: Optional[int] = Field(default=1024, env="OLLAMA_NUM_PREDICT") # Planner LLM settings (optional, defaults to main LLM if not set) planner_provider: Optional[str] = Field(default=None, env="PLANNER_PROVIDER") planner_model_name: Optional[str] = Field(default=None, env="PLANNER_MODEL_NAME") planner_temperature: Optional[float] = Field(default=None, env="PLANNER_TEMPERATURE") planner_base_url: Optional[str] = Field(default=None, env="PLANNER_BASE_URL") planner_api_key: Optional[SecretStr] = Field(default=None, env="PLANNER_API_KEY") class BrowserSettings(BaseSettings): model_config = SettingsConfigDict(env_prefix="MCP_BROWSER_") headless: bool = Field(default=False, env="HEADLESS") # General headless disable_security: bool = Field(default=False, env="DISABLE_SECURITY") # General disable security binary_path: Optional[str] = Field(default=None, env="BINARY_PATH") user_data_dir: Optional[str] = Field(default=None, env="USER_DATA_DIR") window_width: int = Field(default=1280, env="WINDOW_WIDTH") window_height: int = Field(default=1080, env="WINDOW_HEIGHT") use_own_browser: bool = Field(default=False, env="USE_OWN_BROWSER") cdp_url: Optional[str] = Field(default=None, env="CDP_URL") wss_url: Optional[str] = Field(default=None, env="WSS_URL") # For CDP connection if needed keep_open: bool = Field(default=False, env="KEEP_OPEN") # Server-managed browser persistence trace_path: Optional[str] = Field(default=None, env="TRACE_PATH") class AgentToolSettings(BaseSettings): model_config = SettingsConfigDict(env_prefix="MCP_AGENT_TOOL_") max_steps: int = Field(default=100, env="MAX_STEPS") max_actions_per_step: int = Field(default=5, env="MAX_ACTIONS_PER_STEP") tool_calling_method: Optional[str] = Field(default="auto", env="TOOL_CALLING_METHOD") max_input_tokens: Optional[int] = Field(default=128000, env="MAX_INPUT_TOKENS") use_vision: bool = Field(default=True, env="USE_VISION") # Browser settings specific to this tool, can override general MCP_BROWSER_ settings headless: Optional[bool] = Field(default=None, env="HEADLESS") disable_security: Optional[bool] = Field(default=None, env="DISABLE_SECURITY") enable_recording: bool = Field(default=False, env="ENABLE_RECORDING") save_recording_path: Optional[str] = Field(default=None, env="SAVE_RECORDING_PATH") # e.g. ./tmp/recordings history_path: Optional[str] = Field(default=None, env="HISTORY_PATH") # e.g. ./tmp/agent_history class DeepResearchToolSettings(BaseSettings): model_config = SettingsConfigDict(env_prefix="MCP_RESEARCH_TOOL_") max_parallel_browsers: int = Field(default=3, env="MAX_PARALLEL_BROWSERS") save_dir: Optional[str] = Field(default=None, env="SAVE_DIR") # Base dir, task_id will be appended. Optional now. class PathSettings(BaseSettings): model_config = SettingsConfigDict(env_prefix="MCP_PATHS_") downloads: Optional[str] = Field(default=None, env="DOWNLOADS") # e.g. ./tmp/downloads class ServerSettings(BaseSettings): model_config = SettingsConfigDict(env_prefix="MCP_SERVER_") log_file: Optional[str] = Field(default=None, env="LOG_FILE") logging_level: str = Field(default="ERROR", env="LOGGING_LEVEL") anonymized_telemetry: bool = Field(default=True, env="ANONYMIZED_TELEMETRY") mcp_config: Optional[Dict[str, Any]] = Field(default=None, env="MCP_CONFIG") # For controller's MCP client class AppSettings(BaseSettings): model_config = SettingsConfigDict(env_prefix="MCP_", extra='ignore') # Root prefix llm: LLMSettings = Field(default_factory=LLMSettings) browser: BrowserSettings = Field(default_factory=BrowserSettings) agent_tool: AgentToolSettings = Field(default_factory=AgentToolSettings) research_tool: DeepResearchToolSettings = Field(default_factory=DeepResearchToolSettings) paths: PathSettings = Field(default_factory=PathSettings) server: ServerSettings = Field(default_factory=ServerSettings) @field_validator('server', 'llm', 'browser', 'agent_tool', 'research_tool', 'paths', mode='before') @classmethod def ensure_nested_defaults(cls, v: Any) -> Any: # This ensures that even if MCP_SERVER__LOG_FILE is set but MCP_SERVER is not, # the ServerSettings object is still created. # Pydantic-settings usually handles this, but being explicit can help. if v is None: return {} return v def get_api_key_for_provider(self, provider_name: Optional[str], is_planner: bool = False) -> Optional[str]: """Retrieves the API key for a given provider, checking generic, then specific.""" llm_settings_to_use = self.llm provider_to_use = provider_name if provider_name else (self.llm.planner_provider if is_planner else self.llm.provider) if is_planner: if self.llm.planner_api_key: return self.llm.planner_api_key.get_secret_value() # Fallback to main LLM settings if planner-specific key is not set, but provider is if self.llm.planner_provider and not self.llm.planner_api_key: llm_settings_to_use = self.llm # Check main llm settings for this provider # if no planner provider, it will use main llm provider and its key if not provider_to_use: # Should not happen if called correctly return None # Check generic API key first for the relevant LLM settings (main or planner if planner_api_key was set) if not is_planner and llm_settings_to_use.api_key: # only main LLM has generic api_key return llm_settings_to_use.api_key.get_secret_value() provider_specific_key_name = f"{provider_to_use.lower()}_api_key" if hasattr(llm_settings_to_use, provider_specific_key_name): key_val = getattr(llm_settings_to_use, provider_specific_key_name) if key_val and isinstance(key_val, SecretStr): return key_val.get_secret_value() return None def get_endpoint_for_provider(self, provider_name: Optional[str], is_planner: bool = False) -> Optional[str]: """Retrieves the endpoint for a given provider.""" llm_settings_to_use = self.llm provider_to_use = provider_name if provider_name else (self.llm.planner_provider if is_planner else self.llm.provider) if is_planner: if self.llm.planner_base_url: return self.llm.planner_base_url if self.llm.planner_provider and not self.llm.planner_base_url: llm_settings_to_use = self.llm # Check main llm settings for this provider if not provider_to_use: return None if not is_planner and llm_settings_to_use.base_url: # only main LLM has generic base_url return llm_settings_to_use.base_url provider_specific_endpoint_name = f"{provider_to_use.lower()}_endpoint" if hasattr(llm_settings_to_use, provider_specific_endpoint_name): return getattr(llm_settings_to_use, provider_specific_endpoint_name) return None def get_llm_config(self, is_planner: bool = False) -> Dict[str, Any]: """Returns a dictionary of LLM settings suitable for llm_provider.get_llm_model.""" provider = self.llm.planner_provider if is_planner and self.llm.planner_provider else self.llm.provider model_name = self.llm.planner_model_name if is_planner and self.llm.planner_model_name else self.llm.model_name temperature = self.llm.planner_temperature if is_planner and self.llm.planner_temperature is not None else self.llm.temperature api_key = self.get_api_key_for_provider(provider, is_planner=is_planner) base_url = self.get_endpoint_for_provider(provider, is_planner=is_planner) config = { "provider": provider, "model_name": model_name, "temperature": temperature, "api_key": api_key, "base_url": base_url, "use_vision": self.agent_tool.use_vision if not is_planner else False, # Planners typically don't need vision "tool_calling_method": self.agent_tool.tool_calling_method if not is_planner else "auto", "max_input_tokens": self.agent_tool.max_input_tokens if not is_planner else None, } if provider == "azure_openai": config["azure_openai_api_version"] = self.llm.azure_openai_api_version elif provider == "ollama": config["ollama_num_ctx"] = self.llm.ollama_num_ctx config["ollama_num_predict"] = self.llm.ollama_num_predict elif provider == "openrouter": config["provider"] = "openai" return config # Global settings instance, to be imported by other modules settings = AppSettings() # Example usage (for testing this file directly): if __name__ == "__main__": try: print("Loaded AppSettings:") print(settings.model_dump_json(indent=2)) print(f"\nLLM API Key for main provider ({settings.llm.provider}): {settings.get_api_key_for_provider(settings.llm.provider)}") if settings.llm.planner_provider: print(f"LLM API Key for planner provider ({settings.llm.planner_provider}): {settings.get_api_key_for_provider(settings.llm.planner_provider, is_planner=True)}") print("\nMain LLM Config for get_llm_model:") print(settings.get_llm_config()) if settings.llm.planner_provider: print("\nPlanner LLM Config for get_llm_model:") print(settings.get_llm_config(is_planner=True)) except Exception as e: print(f"Error during settings load or test: {e}") import os print("MCP_RESEARCH_TOOL_SAVE_DIR:", os.getenv("MCP_RESEARCH_TOOL_SAVE_DIR"))