Improved prompts to encourage better investigative flow
Improved abstraction
This commit is contained in:
Fahad
138
tools/base.py
138
tools/base.py
@@ -1325,62 +1325,19 @@ When recommending searches, be specific about what information you need and why
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# Extract and validate images from request
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images = getattr(request, "images", None) or []
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# MODEL RESOLUTION NOW HAPPENS AT MCP BOUNDARY
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# Extract pre-resolved model context from server.py
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model_context = self._current_arguments.get("_model_context")
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resolved_model_name = self._current_arguments.get("_resolved_model_name")
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# Use centralized model resolution
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try:
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model_name, model_context = self._resolve_model_context(self._current_arguments, request)
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except ValueError as e:
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# Model resolution failed, return error
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error_output = ToolOutput(
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status="error",
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content=str(e),
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content_type="text",
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)
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return [TextContent(type="text", text=error_output.model_dump_json())]
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if model_context and resolved_model_name:
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# Model was already resolved at MCP boundary
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model_name = resolved_model_name
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logger.debug(f"Using pre-resolved model '{model_name}' from MCP boundary")
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else:
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# Fallback for direct execute calls
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model_name = getattr(request, "model", None)
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if not model_name:
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from config import DEFAULT_MODEL
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model_name = DEFAULT_MODEL
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logger.debug(f"Using fallback model resolution for '{model_name}' (test mode)")
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# For tests: Check if we should require model selection (auto mode)
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if self._should_require_model_selection(model_name):
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# Get suggested model based on tool category
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from providers.registry import ModelProviderRegistry
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tool_category = self.get_model_category()
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suggested_model = ModelProviderRegistry.get_preferred_fallback_model(tool_category)
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# Build error message based on why selection is required
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if model_name.lower() == "auto":
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error_message = (
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f"Model parameter is required in auto mode. "
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f"Suggested model for {self.name}: '{suggested_model}' "
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f"(category: {tool_category.value})"
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)
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else:
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# Model was specified but not available
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available_models = self._get_available_models()
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error_message = (
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f"Model '{model_name}' is not available with current API keys. "
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f"Available models: {', '.join(available_models)}. "
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f"Suggested model for {self.name}: '{suggested_model}' "
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f"(category: {tool_category.value})"
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)
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error_output = ToolOutput(
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status="error",
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content=error_message,
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content_type="text",
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)
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return [TextContent(type="text", text=error_output.model_dump_json())]
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# Create model context for tests
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from utils.model_context import ModelContext
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model_context = ModelContext(model_name)
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# Store resolved model name for use by helper methods
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# Store resolved model name and context for use by helper methods
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self._current_model_name = model_name
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self._model_context = model_context
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@@ -1929,6 +1886,77 @@ When recommending searches, be specific about what information you need and why
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logger.warning(f"Temperature validation failed for {model_name}: {e}")
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return temperature, [f"Temperature validation failed: {e}"]
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def _resolve_model_context(self, arguments: dict[str, Any], request) -> tuple[str, Any]:
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"""
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Resolve model context and name using centralized logic.
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This method extracts the model resolution logic from execute() so it can be
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reused by tools that override execute() (like debug tool) without duplicating code.
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Args:
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arguments: Dictionary of arguments from the MCP client
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request: The validated request object
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Returns:
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tuple[str, ModelContext]: (resolved_model_name, model_context)
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Raises:
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ValueError: If model resolution fails or model selection is required
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"""
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logger = logging.getLogger(f"tools.{self.name}")
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# MODEL RESOLUTION NOW HAPPENS AT MCP BOUNDARY
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# Extract pre-resolved model context from server.py
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model_context = arguments.get("_model_context")
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resolved_model_name = arguments.get("_resolved_model_name")
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if model_context and resolved_model_name:
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# Model was already resolved at MCP boundary
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model_name = resolved_model_name
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logger.debug(f"Using pre-resolved model '{model_name}' from MCP boundary")
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else:
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# Fallback for direct execute calls
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model_name = getattr(request, "model", None)
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if not model_name:
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from config import DEFAULT_MODEL
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model_name = DEFAULT_MODEL
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logger.debug(f"Using fallback model resolution for '{model_name}' (test mode)")
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# For tests: Check if we should require model selection (auto mode)
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if self._should_require_model_selection(model_name):
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# Get suggested model based on tool category
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from providers.registry import ModelProviderRegistry
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tool_category = self.get_model_category()
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suggested_model = ModelProviderRegistry.get_preferred_fallback_model(tool_category)
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# Build error message based on why selection is required
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if model_name.lower() == "auto":
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error_message = (
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f"Model parameter is required in auto mode. "
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f"Suggested model for {self.name}: '{suggested_model}' "
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f"(category: {tool_category.value})"
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)
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else:
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# Model was specified but not available
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available_models = self._get_available_models()
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error_message = (
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f"Model '{model_name}' is not available with current API keys. "
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f"Available models: {', '.join(available_models)}. "
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f"Suggested model for {self.name}: '{suggested_model}' "
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f"(category: {tool_category.value})"
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)
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raise ValueError(error_message)
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# Create model context for tests
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from utils.model_context import ModelContext
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model_context = ModelContext(model_name)
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return model_name, model_context
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def get_model_provider(self, model_name: str) -> ModelProvider:
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"""
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Get a model provider for the specified model.
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