""" Base class for all Zen MCP tools This module provides the abstract base class that all tools must inherit from. It defines the contract that tools must implement and provides common functionality for request validation, error handling, and response formatting. Key responsibilities: - Define the tool interface (abstract methods that must be implemented) - Handle request validation and file path security - Manage Gemini model creation with appropriate configurations - Standardize response formatting and error handling - Support for clarification requests when more information is needed """ import json import logging import os from abc import ABC, abstractmethod from typing import TYPE_CHECKING, Any, Literal, Optional from mcp.types import TextContent from pydantic import BaseModel, Field if TYPE_CHECKING: from tools.models import ToolModelCategory import config from config import MCP_PROMPT_SIZE_LIMIT from providers import ModelProvider, ModelProviderRegistry from utils import check_token_limit from utils.conversation_memory import ( MAX_CONVERSATION_TURNS, add_turn, create_thread, get_conversation_file_list, get_thread, ) from utils.file_storage import FileReference, FileStorage from utils.file_utils import read_file_content, read_files from .models import SPECIAL_STATUS_MODELS, ContinuationOffer, ToolOutput logger = logging.getLogger(__name__) class ToolRequest(BaseModel): """ Base request model for all tools. This Pydantic model defines common parameters that can be used by any tool. Tools can extend this model to add their specific parameters while inheriting these common fields. """ model: Optional[str] = Field( None, description="Model to use. See tool's input schema for available models and their capabilities.", ) temperature: Optional[float] = Field(None, description="Temperature for response (tool-specific defaults)") # Thinking mode controls how much computational budget the model uses for reasoning # Higher values allow for more complex reasoning but increase latency and cost thinking_mode: Optional[Literal["minimal", "low", "medium", "high", "max"]] = Field( None, description=( "Thinking depth: minimal (0.5% of model max), low (8%), medium (33%), high (67%), max (100% of model max)" ), ) use_websearch: Optional[bool] = Field( True, description=( "Enable web search for documentation, best practices, and current information. " "When enabled, the model can request Claude to perform web searches and share results back " "during conversations. Particularly useful for: brainstorming sessions, architectural design " "discussions, exploring industry best practices, working with specific frameworks/technologies, " "researching solutions to complex problems, or when current documentation and community insights " "would enhance the analysis." ), ) continuation_id: Optional[str] = Field( None, description=( "Thread continuation ID for multi-turn conversations. When provided, the complete conversation " "history is automatically embedded as context. Your response should build upon this history " "without repeating previous analysis or instructions. Focus on providing only new insights, " "additional findings, or answers to follow-up questions. Can be used across different tools." ), ) file_handling_mode: Optional[Literal["embedded", "summary", "reference"]] = Field( "embedded", description=( "How to handle file content in responses. 'embedded' includes full content (default), " "'summary' returns only summaries to save tokens, 'reference' stores files and returns IDs." ), ) class BaseTool(ABC): """ Abstract base class for all Gemini tools. This class defines the interface that all tools must implement and provides common functionality for request handling, model creation, and response formatting. To create a new tool: 1. Create a new class that inherits from BaseTool 2. Implement all abstract methods 3. Define a request model that inherits from ToolRequest 4. Register the tool in server.py's TOOLS dictionary """ def __init__(self): # Cache tool metadata at initialization to avoid repeated calls self.name = self.get_name() self.description = self.get_description() self.default_temperature = self.get_default_temperature() # Initialize file storage self.file_storage = FileStorage() def requires_model(self) -> bool: """ Return whether this tool requires AI model access. Tools that override execute() to do pure data processing (like planner) should return False to skip model resolution at the MCP boundary. Returns: True by default, False for tools that don't need model access """ return True # Tool initialization complete @abstractmethod def get_name(self) -> str: """ Return the unique name identifier for this tool. This name is used by MCP clients to invoke the tool and must be unique across all registered tools. Returns: str: The tool's unique name (e.g., "review_code", "analyze") """ pass @abstractmethod def get_description(self) -> str: """ Return a detailed description of what this tool does. This description is shown to MCP clients (like Claude) to help them understand when and how to use the tool. It should be comprehensive and include trigger phrases. Returns: str: Detailed tool description with usage examples """ pass @abstractmethod def get_input_schema(self) -> dict[str, Any]: """ Return the JSON Schema that defines this tool's parameters. This schema is used by MCP clients to validate inputs before sending requests. It should match the tool's request model. Returns: Dict[str, Any]: JSON Schema object defining required and optional parameters """ pass @abstractmethod def get_system_prompt(self) -> str: """ Return the system prompt that configures the AI model's behavior. This prompt sets the context and instructions for how the model should approach the task. It's prepended to the user's request. Returns: str: System prompt with role definition and instructions """ pass def is_effective_auto_mode(self) -> bool: """ Check if we're in effective auto mode for schema generation. This determines whether the model parameter should be required in the tool schema. Used at initialization time when schemas are generated. Returns: bool: True if model parameter should be required in the schema """ from config import DEFAULT_MODEL from providers.registry import ModelProviderRegistry # Case 1: Explicit auto mode if DEFAULT_MODEL.lower() == "auto": return True # Case 2: Model not available (fallback to auto mode) if DEFAULT_MODEL.lower() != "auto": provider = ModelProviderRegistry.get_provider_for_model(DEFAULT_MODEL) if not provider: return True return False def _should_require_model_selection(self, model_name: str) -> bool: """ Check if we should require Claude to select a model at runtime. This is called during request execution to determine if we need to return an error asking Claude to provide a model parameter. Args: model_name: The model name from the request or DEFAULT_MODEL Returns: bool: True if we should require model selection """ # Case 1: Model is explicitly "auto" if model_name.lower() == "auto": return True # Case 2: Requested model is not available from providers.registry import ModelProviderRegistry provider = ModelProviderRegistry.get_provider_for_model(model_name) if not provider: logger = logging.getLogger(f"tools.{self.name}") logger.warning(f"Model '{model_name}' is not available with current API keys. Requiring model selection.") return True return False def _get_available_models(self) -> list[str]: """ Get list of models that are actually available with current API keys. This respects model restrictions automatically. Returns: List of available model names """ from config import MODEL_CAPABILITIES_DESC from providers.base import ProviderType from providers.registry import ModelProviderRegistry # Get available models from registry (respects restrictions) available_models_map = ModelProviderRegistry.get_available_models(respect_restrictions=True) available_models = list(available_models_map.keys()) # Add model aliases if their targets are available model_aliases = [] for alias, target in MODEL_CAPABILITIES_DESC.items(): if alias not in available_models and target in available_models: model_aliases.append(alias) available_models.extend(model_aliases) # Also check if OpenRouter is available (it accepts any model) openrouter_provider = ModelProviderRegistry.get_provider(ProviderType.OPENROUTER) if openrouter_provider and not available_models: # If only OpenRouter is available, suggest using any model through it available_models.append("any model via OpenRouter") if not available_models: # Check if it's due to restrictions from utils.model_restrictions import get_restriction_service restriction_service = get_restriction_service() restrictions = restriction_service.get_restriction_summary() if restrictions: return ["none - all models blocked by restrictions set in .env"] else: return ["none - please configure API keys"] return available_models def get_model_field_schema(self) -> dict[str, Any]: """ Generate the model field schema based on auto mode configuration. When auto mode is enabled, the model parameter becomes required and includes detailed descriptions of each model's capabilities. Returns: Dict containing the model field JSON schema """ import os from config import DEFAULT_MODEL, MODEL_CAPABILITIES_DESC # Check if OpenRouter is configured has_openrouter = bool( os.getenv("OPENROUTER_API_KEY") and os.getenv("OPENROUTER_API_KEY") != "your_openrouter_api_key_here" ) # Use the centralized effective auto mode check if self.is_effective_auto_mode(): # In auto mode, model is required and we provide detailed descriptions model_desc_parts = [ "IMPORTANT: Use the model specified by the user if provided, OR select the most suitable model " "for this specific task based on the requirements and capabilities listed below:" ] # Only show descriptions for available models available_models = self._get_available_models() for model, desc in MODEL_CAPABILITIES_DESC.items(): if model in available_models: model_desc_parts.append(f"- '{model}': {desc}") if has_openrouter: # Add OpenRouter models with descriptions try: import logging from providers.openrouter_registry import OpenRouterModelRegistry registry = OpenRouterModelRegistry() # Group models by their model_name to avoid duplicates seen_models = set() model_configs = [] for alias in registry.list_aliases(): config = registry.resolve(alias) if config and config.model_name not in seen_models: seen_models.add(config.model_name) model_configs.append((alias, config)) # Sort by context window (descending) then by alias model_configs.sort(key=lambda x: (-x[1].context_window, x[0])) if model_configs: model_desc_parts.append("\nOpenRouter models (use these aliases):") for alias, config in model_configs[:10]: # Limit to top 10 # Format context window in human-readable form context_tokens = config.context_window if context_tokens >= 1_000_000: context_str = f"{context_tokens // 1_000_000}M" elif context_tokens >= 1_000: context_str = f"{context_tokens // 1_000}K" else: context_str = str(context_tokens) # Build description line if config.description: desc = f"- '{alias}' ({context_str} context): {config.description}" else: # Fallback to showing the model name if no description desc = f"- '{alias}' ({context_str} context): {config.model_name}" model_desc_parts.append(desc) # Add note about additional models if any were cut off total_models = len(model_configs) if total_models > 10: model_desc_parts.append(f"... and {total_models - 10} more models available") except Exception as e: # Log for debugging but don't fail import logging logging.debug(f"Failed to load OpenRouter model descriptions: {e}") # Fallback to simple message model_desc_parts.append( "\nOpenRouter models: If configured, you can also use ANY model available on OpenRouter." ) # Get only models from enabled providers available_models = self._get_available_models() return { "type": "string", "description": "\n".join(model_desc_parts), "enum": available_models, } else: # Normal mode - model is optional with default available_models = self._get_available_models() models_str = ", ".join(f"'{m}'" for m in available_models) description = f"Model to use. Native models: {models_str}." if has_openrouter: # Add OpenRouter aliases try: # Import registry directly to show available aliases # This works even without an API key from providers.openrouter_registry import OpenRouterModelRegistry registry = OpenRouterModelRegistry() aliases = registry.list_aliases() # Show ALL aliases from the configuration if aliases: # Show all aliases so Claude knows every option available all_aliases = sorted(aliases) alias_list = ", ".join(f"'{a}'" for a in all_aliases) description += f" OpenRouter aliases: {alias_list}." else: description += " OpenRouter: Any model available on openrouter.ai." except Exception: description += ( " OpenRouter: Any model available on openrouter.ai " "(e.g., 'gpt-4', 'claude-3-opus', 'mistral-large')." ) description += f" Defaults to '{DEFAULT_MODEL}' if not specified." return { "type": "string", "description": description, } def get_default_temperature(self) -> float: """ Return the default temperature setting for this tool. Override this method to set tool-specific temperature defaults. Lower values (0.0-0.3) for analytical tasks, higher (0.7-1.0) for creative tasks. Returns: float: Default temperature between 0.0 and 1.0 """ return 0.5 def wants_line_numbers_by_default(self) -> bool: """ Return whether this tool wants line numbers added to code files by default. By default, ALL tools get line numbers for precise code references. Line numbers are essential for accurate communication about code locations. Line numbers add ~8-10% token overhead but provide precise targeting for: - Code review feedback ("SQL injection on line 45") - Debug error locations ("Memory leak in loop at lines 123-156") - Test generation targets ("Generate tests for method at lines 78-95") - Refactoring guidance ("Extract method from lines 67-89") - General code discussions ("Where is X defined?" -> "Line 42") The only exception is when reading diffs, which have their own line markers. Returns: bool: True if line numbers should be added by default for this tool """ return True # All tools get line numbers by default for consistency def get_default_thinking_mode(self) -> str: """ Return the default thinking mode for this tool. Thinking mode controls computational budget for reasoning. Override for tools that need more or less reasoning depth. Returns: str: One of "minimal", "low", "medium", "high", "max" """ return "medium" # Default to medium thinking for better reasoning def get_model_category(self) -> "ToolModelCategory": """ Return the model category for this tool. Model category influences which model is selected in auto mode. Override to specify whether your tool needs extended reasoning, fast response, or balanced capabilities. Returns: ToolModelCategory: Category that influences model selection """ from tools.models import ToolModelCategory return ToolModelCategory.BALANCED def get_conversation_embedded_files(self, continuation_id: Optional[str]) -> list[str]: """ Get list of files already embedded in conversation history. This method returns the list of files that have already been embedded in the conversation history for a given continuation thread. Tools can use this to avoid re-embedding files that are already available in the conversation context. Args: continuation_id: Thread continuation ID, or None for new conversations Returns: list[str]: List of file paths already embedded in conversation history """ if not continuation_id: # New conversation, no files embedded yet return [] thread_context = get_thread(continuation_id) if not thread_context: # Thread not found, no files embedded return [] embedded_files = get_conversation_file_list(thread_context) logger.debug(f"[FILES] {self.name}: Found {len(embedded_files)} embedded files") return embedded_files def filter_new_files(self, requested_files: list[str], continuation_id: Optional[str]) -> list[str]: """ Filter out files that are already embedded in conversation history. This method prevents duplicate file embeddings by filtering out files that have already been embedded in the conversation history. This optimizes token usage while ensuring tools still have logical access to all requested files through conversation history references. Args: requested_files: List of files requested for current tool execution continuation_id: Thread continuation ID, or None for new conversations Returns: list[str]: List of files that need to be embedded (not already in history) """ logger.debug(f"[FILES] {self.name}: Filtering {len(requested_files)} requested files") if not continuation_id: # New conversation, all files are new logger.debug(f"[FILES] {self.name}: New conversation, all {len(requested_files)} files are new") return requested_files try: embedded_files = set(self.get_conversation_embedded_files(continuation_id)) logger.debug(f"[FILES] {self.name}: Found {len(embedded_files)} embedded files in conversation") # Safety check: If no files are marked as embedded but we have a continuation_id, # this might indicate an issue with conversation history. Be conservative. if not embedded_files: logger.debug(f"{self.name} tool: No files found in conversation history for thread {continuation_id}") logger.debug( f"[FILES] {self.name}: No embedded files found, returning all {len(requested_files)} requested files" ) return requested_files # Return only files that haven't been embedded yet new_files = [f for f in requested_files if f not in embedded_files] logger.debug( f"[FILES] {self.name}: After filtering: {len(new_files)} new files, {len(requested_files) - len(new_files)} already embedded" ) logger.debug(f"[FILES] {self.name}: New files to embed: {new_files}") # Log filtering results for debugging if len(new_files) < len(requested_files): skipped = [f for f in requested_files if f in embedded_files] logger.debug( f"{self.name} tool: Filtering {len(skipped)} files already in conversation history: {', '.join(skipped)}" ) logger.debug(f"[FILES] {self.name}: Skipped (already embedded): {skipped}") return new_files except Exception as e: # If there's any issue with conversation history lookup, be conservative # and include all files rather than risk losing access to needed files logger.warning(f"{self.name} tool: Error checking conversation history for {continuation_id}: {e}") logger.warning(f"{self.name} tool: Including all requested files as fallback") logger.debug( f"[FILES] {self.name}: Exception in filter_new_files, returning all {len(requested_files)} files as fallback" ) return requested_files def _prepare_file_content_for_prompt( self, request_files: list[str], continuation_id: Optional[str], context_description: str = "New files", max_tokens: Optional[int] = None, reserve_tokens: int = 1_000, remaining_budget: Optional[int] = None, arguments: Optional[dict] = None, file_handling_mode: Optional[str] = None, model_context: Optional[Any] = None, ) -> tuple[str, list[str], Optional[list[FileReference]]]: """ Centralized file processing for tool prompts. This method handles the common pattern across all tools: 1. Filter out files already embedded in conversation history 2. Read content of only new files 3. Generate informative note about skipped files 4. Handle different file handling modes (embedded, summary, reference) Args: request_files: List of files requested for current tool execution continuation_id: Thread continuation ID, or None for new conversations context_description: Description for token limit validation (e.g. "Code", "New files") max_tokens: Maximum tokens to use (defaults to remaining budget or model-specific content allocation) reserve_tokens: Tokens to reserve for additional prompt content (default 1K) remaining_budget: Remaining token budget after conversation history (from server.py) arguments: Original tool arguments (used to extract _remaining_tokens if available) file_handling_mode: How to handle files - "embedded" (default), "summary", or "reference" Returns: tuple[str, list[str], Optional[list[FileReference]]]: - formatted_file_content: Formatted file content string ready for prompt inclusion - actually_processed_files: List of individual file paths that were actually read and embedded (directories are expanded to individual files) - file_references: List of FileReference objects if mode is "summary" or "reference" Note: This method now separates image files and stores them in self._current_images for use by the execute method when calling generate_content """ if not request_files: return "", [], None # Note: Even if conversation history is already embedded, we still need to process # any NEW files that aren't in the conversation history yet. The filter_new_files # method will correctly identify which files need to be embedded. # Extract remaining budget from arguments if available if remaining_budget is None: # Use provided arguments or fall back to stored arguments from execute() args_to_use = arguments or getattr(self, "_current_arguments", {}) remaining_budget = args_to_use.get("_remaining_tokens") # Use remaining budget if provided, otherwise fall back to max_tokens or model-specific default if remaining_budget is not None: effective_max_tokens = remaining_budget - reserve_tokens elif max_tokens is not None: effective_max_tokens = max_tokens - reserve_tokens else: # Get model-specific limits # First check if model_context was passed from server.py model_context = None if arguments: model_context = arguments.get("_model_context") or getattr(self, "_current_arguments", {}).get( "_model_context" ) if model_context: # Use the passed model context try: token_allocation = model_context.calculate_token_allocation() effective_max_tokens = token_allocation.file_tokens - reserve_tokens logger.debug( f"[FILES] {self.name}: Using passed model context for {model_context.model_name}: " f"{token_allocation.file_tokens:,} file tokens from {token_allocation.total_tokens:,} total" ) except Exception as e: logger.warning(f"[FILES] {self.name}: Error using passed model context: {e}") # Fall through to manual calculation model_context = None if not model_context: # Manual calculation as fallback from config import DEFAULT_MODEL model_name = getattr(self, "_current_model_name", None) or DEFAULT_MODEL # Handle auto mode gracefully if model_name.lower() == "auto": from providers.registry import ModelProviderRegistry # Use tool-specific fallback model for capacity estimation # This properly handles different providers (OpenAI=200K, Gemini=1M) tool_category = self.get_model_category() fallback_model = ModelProviderRegistry.get_preferred_fallback_model(tool_category) logger.debug( f"[FILES] {self.name}: Auto mode detected, using {fallback_model} " f"for {tool_category.value} tool capacity estimation" ) try: provider = self.get_model_provider(fallback_model) capabilities = provider.get_capabilities(fallback_model) # Calculate content allocation based on model capacity if capabilities.context_window < 300_000: # Smaller context models: 60% content, 40% response model_content_tokens = int(capabilities.context_window * 0.6) else: # Larger context models: 80% content, 20% response model_content_tokens = int(capabilities.context_window * 0.8) effective_max_tokens = model_content_tokens - reserve_tokens logger.debug( f"[FILES] {self.name}: Using {fallback_model} capacity for auto mode: " f"{model_content_tokens:,} content tokens from {capabilities.context_window:,} total" ) except (ValueError, AttributeError) as e: # Handle specific errors: provider not found, model not supported, missing attributes logger.warning( f"[FILES] {self.name}: Could not get capabilities for fallback model {fallback_model}: {type(e).__name__}: {e}" ) # Fall back to conservative default for safety effective_max_tokens = 100_000 - reserve_tokens except Exception as e: # Catch any other unexpected errors logger.error( f"[FILES] {self.name}: Unexpected error getting model capabilities: {type(e).__name__}: {e}" ) effective_max_tokens = 100_000 - reserve_tokens else: # Normal mode - use the specified model try: provider = self.get_model_provider(model_name) capabilities = provider.get_capabilities(model_name) # Calculate content allocation based on model capacity if capabilities.context_window < 300_000: # Smaller context models: 60% content, 40% response model_content_tokens = int(capabilities.context_window * 0.6) else: # Larger context models: 80% content, 20% response model_content_tokens = int(capabilities.context_window * 0.8) effective_max_tokens = model_content_tokens - reserve_tokens logger.debug( f"[FILES] {self.name}: Using model-specific limit for {model_name}: " f"{model_content_tokens:,} content tokens from {capabilities.context_window:,} total" ) except (ValueError, AttributeError) as e: # Handle specific errors: provider not found, model not supported, missing attributes logger.warning( f"[FILES] {self.name}: Could not get model capabilities for {model_name}: {type(e).__name__}: {e}" ) # Fall back to conservative default for safety effective_max_tokens = 100_000 - reserve_tokens except Exception as e: # Catch any other unexpected errors logger.error( f"[FILES] {self.name}: Unexpected error getting model capabilities: {type(e).__name__}: {e}" ) effective_max_tokens = 100_000 - reserve_tokens # Ensure we have a reasonable minimum budget effective_max_tokens = max(1000, effective_max_tokens) files_to_embed = self.filter_new_files(request_files, continuation_id) logger.debug(f"[FILES] {self.name}: Will embed {len(files_to_embed)} files after filtering") # Log the specific files for debugging/testing if files_to_embed: logger.info( f"[FILE_PROCESSING] {self.name} tool will embed new files: {', '.join([os.path.basename(f) for f in files_to_embed])}" ) else: logger.info( f"[FILE_PROCESSING] {self.name} tool: No new files to embed (all files already in conversation history)" ) content_parts = [] actually_processed_files = [] # Separate images from text files from utils.file_types import IMAGE_EXTENSIONS text_files = [] image_files = [] for file_path in files_to_embed: if any(file_path.lower().endswith(ext) for ext in IMAGE_EXTENSIONS): image_files.append(file_path) else: text_files.append(file_path) # Store images for later use by execute method self._current_images = image_files logger.debug(f"[FILES] {self.name}: Separated {len(text_files)} text files and {len(image_files)} image files") # Read content of new text files only (images are handled separately) if text_files: logger.debug(f"{self.name} tool embedding {len(text_files)} new text files: {', '.join(text_files)}") logger.debug( f"[FILES] {self.name}: Starting file embedding with token budget {effective_max_tokens + reserve_tokens:,}" ) try: # Before calling read_files, expand directories to get individual file paths from utils.file_utils import expand_paths expanded_files = expand_paths(text_files) logger.debug( f"[FILES] {self.name}: Expanded {len(text_files)} text file paths to {len(expanded_files)} individual files" ) file_content = read_files( text_files, max_tokens=effective_max_tokens + reserve_tokens, reserve_tokens=reserve_tokens, include_line_numbers=self.wants_line_numbers_by_default(), ) self._validate_token_limit(file_content, context_description) content_parts.append(file_content) # Track the expanded files as actually processed actually_processed_files.extend(expanded_files) # Estimate tokens for debug logging from utils.token_utils import estimate_tokens content_tokens = estimate_tokens(file_content) logger.debug( f"{self.name} tool successfully embedded {len(files_to_embed)} files ({content_tokens:,} tokens)" ) logger.debug(f"[FILES] {self.name}: Successfully embedded files - {content_tokens:,} tokens used") logger.debug( f"[FILES] {self.name}: Actually processed {len(actually_processed_files)} individual files" ) except Exception as e: logger.error(f"{self.name} tool failed to embed files {files_to_embed}: {type(e).__name__}: {e}") logger.debug(f"[FILES] {self.name}: File embedding failed - {type(e).__name__}: {e}") raise else: logger.debug(f"[FILES] {self.name}: No files to embed after filtering") # Add note about image files if any if image_files: image_note = "\n\n--- IMAGE FILES ---\n" image_note += f"The following {len(image_files)} image files will be processed by the vision model:\n" for img in image_files: image_note += f" - {img}\n" image_note += "--- END IMAGE FILES ---\n" content_parts.append(image_note) actually_processed_files.extend(image_files) # Generate note about files already in conversation history if continuation_id and len(files_to_embed) < len(request_files): embedded_files = self.get_conversation_embedded_files(continuation_id) skipped_files = [f for f in request_files if f in embedded_files] if skipped_files: logger.debug( f"{self.name} tool skipping {len(skipped_files)} files already in conversation history: {', '.join(skipped_files)}" ) logger.debug(f"[FILES] {self.name}: Adding note about {len(skipped_files)} skipped files") if content_parts: content_parts.append("\n\n") note_lines = [ "--- NOTE: Additional files referenced in conversation history ---", "The following files are already available in our conversation context:", "\n".join(f" - {f}" for f in skipped_files), "--- END NOTE ---", ] content_parts.append("\n".join(note_lines)) else: logger.debug(f"[FILES] {self.name}: No skipped files to note") # Determine file handling mode if file_handling_mode is None: # Try to get from arguments args_to_use = arguments or getattr(self, "_current_arguments", {}) file_handling_mode = args_to_use.get("file_handling_mode", "embedded") # Handle different file modes file_references = None if file_handling_mode == "embedded": # Default behavior - return full content result = "".join(content_parts) if content_parts else "" logger.debug( f"[FILES] {self.name}: _prepare_file_content_for_prompt returning {len(result)} chars, {len(actually_processed_files)} processed files" ) return result, actually_processed_files, None elif file_handling_mode in ["summary", "reference"]: # Store files and get references files_content = {} for file_path in actually_processed_files: # Read individual file content file_content, _ = read_file_content(file_path) if file_content: files_content[file_path] = file_content # Store files and get references file_references = self._store_files_for_reference(files_content, continuation_id) if file_handling_mode == "summary": # Return summaries in the prompt for AI to analyze summary_parts = [] summary_parts.append("\n=== FILE SUMMARIES ===\n") summary_parts.append("The following files have been stored for analysis:\n\n") for ref in file_references: summary_parts.append(f"📄 {ref.file_path}\n") summary_parts.append(f" Summary: {ref.summary}\n") summary_parts.append(f" Reference ID: {ref.reference_id}\n") summary_parts.append(f" Size: {ref.size:,} bytes\n\n") summary_parts.append("=== END FILE SUMMARIES ===\n") # For summary mode, still include full content for AI analysis # but indicate that Claude will only see summaries full_prompt_parts = content_parts + [ "\n\nNOTE: The above files are provided for your analysis. " + "Claude will only receive file summaries in the response, not the full content.\n" ] result = "".join(full_prompt_parts) if full_prompt_parts else "" logger.debug( f"[FILES] {self.name}: Summary mode - stored {len(file_references)} files, returning {len(result)} chars for AI analysis" ) return result, actually_processed_files, file_references else: # reference mode # Only return reference information ref_parts = [] ref_parts.append("\n=== FILE REFERENCES ===\n") ref_parts.append(f"The following {len(file_references)} files have been stored:\n\n") for ref in file_references: ref_parts.append(f"- {ref.file_path} (ID: {ref.reference_id})\n") ref_parts.append("\n=== END FILE REFERENCES ===\n") ref_parts.append( "\nNOTE: Full file contents are stored and can be retrieved using the reference IDs.\n" ) # Still include full content for AI but with a note full_prompt_parts = content_parts + [ "\n\nNOTE: The above files are provided for your analysis. " + "Claude will only receive file reference IDs in the response, not the content.\n" ] result = "".join(full_prompt_parts) if full_prompt_parts else "" logger.debug(f"[FILES] {self.name}: Reference mode - stored {len(file_references)} files") return result, actually_processed_files, file_references # Default fallback result = "".join(content_parts) if content_parts else "" return result, actually_processed_files, None def _store_file_references(self, file_references: Optional[list[FileReference]]) -> None: """Store file references for later use in response.""" if file_references: self._current_file_references = file_references def _generate_file_summary(self, file_path: str, content: str) -> str: """ Generate a summary for a file based on its content. Args: file_path: Path to the file content: File content Returns: str: Brief summary of the file """ import os # Get file extension and name basename = os.path.basename(file_path) _, ext = os.path.splitext(basename) # Count lines and estimate complexity lines = content.split("\n") line_count = len(lines) # Determine file type file_type = "file" if ext in [".py"]: file_type = "Python module" # Count functions and classes import_count = sum(1 for line in lines if line.strip().startswith(("import ", "from "))) class_count = sum(1 for line in lines if line.strip().startswith("class ")) func_count = sum(1 for line in lines if line.strip().startswith("def ")) summary = f"{file_type} with {line_count} lines, {import_count} imports, {class_count} classes, {func_count} functions" elif ext in [".js", ".ts", ".jsx", ".tsx"]: file_type = f"JavaScript/TypeScript {ext} file" summary = f"{file_type} with {line_count} lines" elif ext in [".java"]: file_type = "Java source file" class_count = sum(1 for line in lines if "class " in line and "{" in line) summary = f"{file_type} with {line_count} lines, {class_count} classes" elif ext in [".cpp", ".c", ".h", ".hpp"]: file_type = "C/C++ source file" summary = f"{file_type} with {line_count} lines" elif ext in [".go"]: file_type = "Go source file" func_count = sum(1 for line in lines if line.strip().startswith("func ")) summary = f"{file_type} with {line_count} lines, {func_count} functions" elif ext in [".rs"]: file_type = "Rust source file" fn_count = sum(1 for line in lines if line.strip().startswith("fn ")) summary = f"{file_type} with {line_count} lines, {fn_count} functions" elif ext in [".md"]: file_type = "Markdown documentation" # Count headers header_count = sum(1 for line in lines if line.strip().startswith("#")) summary = f"{file_type} with {line_count} lines, {header_count} sections" elif ext in [".json"]: file_type = "JSON data file" summary = f"{file_type} with {line_count} lines" elif ext in [".yaml", ".yml"]: file_type = "YAML configuration" summary = f"{file_type} with {line_count} lines" elif ext in [".txt"]: file_type = "Text file" word_count = len(content.split()) summary = f"{file_type} with {line_count} lines, ~{word_count} words" else: summary = f"{basename}: {line_count} lines" return summary def _store_files_for_reference( self, files_content: dict[str, str], continuation_id: Optional[str] = None ) -> list[FileReference]: """ Store files and return references. Args: files_content: Dict of file_path -> content continuation_id: Optional thread ID for context Returns: list[FileReference]: List of stored file references """ references = [] for file_path, content in files_content.items(): # Generate summary summary = self._generate_file_summary(file_path, content) # Store file metadata = { "tool": self.name, "continuation_id": continuation_id, } file_ref = self.file_storage.store_file( file_path=file_path, content=content, summary=summary, metadata=metadata ) references.append(file_ref) logger.debug(f"[FILES] Stored file {file_path} with reference {file_ref.reference_id}") return references def get_websearch_instruction(self, use_websearch: bool, tool_specific: Optional[str] = None) -> str: """ Generate standardized web search instruction based on the use_websearch parameter. Args: use_websearch: Whether web search is enabled tool_specific: Optional tool-specific search guidance Returns: str: Web search instruction to append to prompt, or empty string """ if not use_websearch: return "" base_instruction = """ WEB SEARCH CAPABILITY: You can request Claude to perform web searches to enhance your analysis with current information! IMPORTANT: When you identify areas where web searches would significantly improve your response (such as checking current documentation, finding recent solutions, verifying best practices, or gathering community insights), you MUST explicitly instruct Claude to perform specific web searches and then respond back using the continuation_id from this response to continue the analysis. Use clear, direct language based on the value of the search: For valuable supplementary information: "Please perform a web search on '[specific topic/query]' and then continue this analysis using the continuation_id from this response if you find relevant information." For important missing information: "Please search for '[specific topic/query]' and respond back with the findings using the continuation_id from this response - this information is needed to provide a complete analysis." For critical/essential information: "SEARCH REQUIRED: Please immediately perform a web search on '[specific topic/query]' and respond back with the results using the continuation_id from this response. Cannot provide accurate analysis without this current information." This ensures you get the most current and comprehensive information while maintaining conversation context through the continuation_id.""" if tool_specific: return f"""{base_instruction} {tool_specific} When recommending searches, be specific about what information you need and why it would improve your analysis.""" # Default instruction for all tools return f"""{base_instruction} Consider requesting searches for: - Current documentation and API references - Recent best practices and patterns - Known issues and community solutions - Framework updates and compatibility - Security advisories and patches - Performance benchmarks and optimizations When recommending searches, be specific about what information you need and why it would improve your analysis. Always remember to instruct Claude to use the continuation_id from this response when providing search results.""" @abstractmethod def get_request_model(self): """ Return the Pydantic model class used for validating requests. This model should inherit from ToolRequest and define all parameters specific to this tool. Returns: Type[ToolRequest]: The request model class """ pass def validate_file_paths(self, request) -> Optional[str]: """ Validate that all file paths in the request are absolute. This is a critical security function that prevents path traversal attacks and ensures all file access is properly controlled. All file paths must be absolute to avoid ambiguity and security issues. Args: request: The validated request object Returns: Optional[str]: Error message if validation fails, None if all paths are valid """ # Check if request has 'files' attribute (used by most tools) if hasattr(request, "files") and request.files: for file_path in request.files: if not os.path.isabs(file_path): return ( f"Error: All file paths must be absolute. " f"Received relative path: {file_path}\n" f"Please provide the full absolute path starting with '/'" ) # Check if request has 'path' attribute (used by review_changes tool) if hasattr(request, "path") and request.path: if not os.path.isabs(request.path): return ( f"Error: Path must be absolute. " f"Received relative path: {request.path}\n" f"Please provide the full absolute path starting with '/'" ) return None def check_prompt_size(self, text: str) -> Optional[dict[str, Any]]: """ Check if USER INPUT text is too large for MCP transport boundary. IMPORTANT: This method should ONLY be used to validate user input that crosses the Claude CLI ↔ MCP Server transport boundary. It should NOT be used to limit internal MCP Server operations. MCP Protocol Boundaries: Claude CLI ←→ MCP Server ←→ External Model ↑ ↑ This limit applies here This is NOT limited The MCP protocol has a combined request+response limit of ~25K tokens. To ensure adequate space for MCP Server → Claude CLI responses, we limit user input to 50K characters (roughly ~10-12K tokens). Larger user prompts are handled by having Claude save them to prompt.txt files, bypassing MCP's transport constraints while preserving response capacity. What should be checked with this method: - request.prompt field (user input from Claude CLI) - prompt.txt file content (alternative user input) - Other direct user input fields What should NOT be checked with this method: - System prompts added internally - File content embedded by tools - Conversation history from Redis - Complete prompts sent to external models Args: text: The user input text to check (NOT internal prompt content) Returns: Optional[Dict[str, Any]]: Response asking for file handling if too large, None otherwise """ if text and len(text) > MCP_PROMPT_SIZE_LIMIT: return { "status": "resend_prompt", "content": ( f"MANDATORY ACTION REQUIRED: The prompt is too large for MCP's token limits (>{MCP_PROMPT_SIZE_LIMIT:,} characters). " "YOU MUST IMMEDIATELY save the prompt text to a temporary file named 'prompt.txt' in the working directory. " "DO NOT attempt to shorten or modify the prompt. SAVE IT AS-IS to 'prompt.txt'. " "Then resend the request with the absolute file path to 'prompt.txt' in the files parameter, " "along with any other files you wish to share as context. Leave the prompt text itself empty or very brief in the new request. " "This is the ONLY way to handle large prompts - you MUST follow these exact steps." ), "content_type": "text", "metadata": { "prompt_size": len(text), "limit": MCP_PROMPT_SIZE_LIMIT, "instructions": "MANDATORY: Save prompt to 'prompt.txt' in current folder and include absolute path in files parameter. DO NOT modify or shorten the prompt.", }, } return None def handle_prompt_file(self, files: Optional[list[str]]) -> tuple[Optional[str], Optional[list[str]]]: """ Check for and handle prompt.txt in the files list. If prompt.txt is found, reads its content and removes it from the files list. This file is treated specially as the main prompt, not as an embedded file. This mechanism allows us to work around MCP's ~25K token limit by having Claude save large prompts to a file, effectively using the file transfer mechanism to bypass token constraints while preserving response capacity. Args: files: List of file paths (will be translated for current environment) Returns: tuple: (prompt_content, updated_files_list) """ if not files: return None, files prompt_content = None updated_files = [] for file_path in files: # Check if the filename is exactly "prompt.txt" # This ensures we don't match files like "myprompt.txt" or "prompt.txt.bak" if os.path.basename(file_path) == "prompt.txt": try: # Read prompt.txt content and extract just the text content, _ = read_file_content(file_path) # Extract the content between the file markers if "--- BEGIN FILE:" in content and "--- END FILE:" in content: lines = content.split("\n") in_content = False content_lines = [] for line in lines: if line.startswith("--- BEGIN FILE:"): in_content = True continue elif line.startswith("--- END FILE:"): break elif in_content: content_lines.append(line) prompt_content = "\n".join(content_lines) else: # Fallback: if it's already raw content (from tests or direct input) # and doesn't have error markers, use it directly if not content.startswith("\n--- ERROR"): prompt_content = content else: prompt_content = None except Exception: # If we can't read the file, we'll just skip it # The error will be handled elsewhere pass else: # Keep the original path in the files list (will be translated later by read_files) updated_files.append(file_path) return prompt_content, updated_files if updated_files else None async def execute(self, arguments: dict[str, Any]) -> list[TextContent]: """ Execute the tool with the provided arguments. This is the main entry point for tool execution. It handles: 1. Request validation using the tool's Pydantic model 2. File path security validation 3. Prompt preparation 4. Model creation and configuration 5. Response generation and formatting 6. Error handling and recovery Args: arguments: Dictionary of arguments from the MCP client Returns: List[TextContent]: Formatted response as MCP TextContent objects """ try: # Store arguments for access by helper methods (like _prepare_file_content_for_prompt) self._current_arguments = arguments # Set up logger for this tool execution logger = logging.getLogger(f"tools.{self.name}") logger.info(f"🔧 {self.name} tool called with arguments: {list(arguments.keys())}") # Validate request using the tool's Pydantic model # This ensures all required fields are present and properly typed request_model = self.get_request_model() request = request_model(**arguments) logger.debug(f"Request validation successful for {self.name}") # Validate file paths for security # This prevents path traversal attacks and ensures proper access control path_error = self.validate_file_paths(request) if path_error: error_output = ToolOutput( status="error", content=path_error, content_type="text", ) return [TextContent(type="text", text=error_output.model_dump_json())] # Check if we have continuation_id - if so, conversation history is already embedded continuation_id = getattr(request, "continuation_id", None) if continuation_id: # When continuation_id is present, server.py has already injected the # conversation history into the appropriate field. We need to check if # the prompt already contains conversation history marker. logger.debug(f"Continuing {self.name} conversation with thread {continuation_id}") # Store the original arguments to detect enhanced prompts self._has_embedded_history = False # Check if conversation history is already embedded in the prompt field field_value = getattr(request, "prompt", "") field_name = "prompt" if "=== CONVERSATION HISTORY ===" in field_value: # Conversation history is already embedded, use it directly prompt = field_value self._has_embedded_history = True logger.debug(f"{self.name}: Using pre-embedded conversation history from {field_name}") else: # No embedded history, prepare prompt normally prompt = await self.prepare_prompt(request) logger.debug(f"{self.name}: No embedded history found, prepared prompt normally") else: # New conversation, prepare prompt normally prompt = await self.prepare_prompt(request) # Add follow-up instructions for new conversations from server import get_follow_up_instructions follow_up_instructions = get_follow_up_instructions(0) # New conversation, turn 0 prompt = f"{prompt}\n\n{follow_up_instructions}" logger.debug(f"Added follow-up instructions for new {self.name} conversation") # Extract model configuration from request or use defaults model_name = getattr(request, "model", None) if not model_name: from config import DEFAULT_MODEL model_name = DEFAULT_MODEL # Check if we need Claude to select a model # This happens when: # 1. The model is explicitly "auto" # 2. The requested model is not available if self._should_require_model_selection(model_name): # Get suggested model based on tool category from providers.registry import ModelProviderRegistry tool_category = self.get_model_category() suggested_model = ModelProviderRegistry.get_preferred_fallback_model(tool_category) # Build error message based on why selection is required if model_name.lower() == "auto": error_message = ( f"Model parameter is required in auto mode. " f"Suggested model for {self.name}: '{suggested_model}' " f"(category: {tool_category.value})" ) else: # Model was specified but not available # Get list of available models available_models = self._get_available_models() error_message = ( f"Model '{model_name}' is not available with current API keys. " f"Available models: {', '.join(available_models)}. " f"Suggested model for {self.name}: '{suggested_model}' " f"(category: {tool_category.value})" ) error_output = ToolOutput( status="error", content=error_message, content_type="text", ) return [TextContent(type="text", text=error_output.model_dump_json())] # Store model name for use by helper methods like _prepare_file_content_for_prompt # Only set this after auto mode validation to prevent "auto" being used as a model name self._current_model_name = model_name temperature = getattr(request, "temperature", None) if temperature is None: temperature = self.get_default_temperature() thinking_mode = getattr(request, "thinking_mode", None) if thinking_mode is None: thinking_mode = self.get_default_thinking_mode() # Get the appropriate model provider provider = self.get_model_provider(model_name) # Validate and correct temperature for this model temperature, temp_warnings = self._validate_and_correct_temperature(model_name, temperature) # Log any temperature corrections for warning in temp_warnings: logger.warning(warning) # Get system prompt for this tool system_prompt = self.get_system_prompt() # Generate AI response using the provider logger.info(f"Sending request to {provider.get_provider_type().value} API for {self.name}") logger.info(f"Using model: {model_name} via {provider.get_provider_type().value} provider") logger.debug(f"Prompt length: {len(prompt)} characters") # Generate content with provider abstraction # Add service_tier for OpenAI models if using flex processing generation_kwargs = {} if ( provider.get_provider_type().value == "openai" and model_name in ["o3", "o3-mini"] and config.OPENAI_USE_FLEX_PROCESSING ): # Use flex service tier for OpenAI models to reduce costs generation_kwargs["service_tier"] = "flex" logger.info(f"Using Flex Processing service tier for OpenAI model {model_name}") # Get images if any were separated during file processing images = getattr(self, "_current_images", None) model_response = provider.generate_content( prompt=prompt, model_name=model_name, system_prompt=system_prompt, temperature=temperature, thinking_mode=thinking_mode if provider.supports_thinking_mode(model_name) else None, images=images, **generation_kwargs, ) logger.info(f"Received response from {provider.get_provider_type().value} API for {self.name}") # Log raw response content for debugging if model_response.content: logger.debug(f"[{self.name.upper()} RESPONSE] Content length: {len(model_response.content)} chars") logger.debug(f"[{self.name.upper()} RESPONSE] First 500 chars: {model_response.content[:500]}...") if not model_response.content.strip(): logger.warning(f"[{self.name.upper()} RESPONSE] Response content is empty or whitespace only!") else: logger.warning(f"[{self.name.upper()} RESPONSE] No content in response object!") # Check if we fell back from flex tier if model_response.metadata.get("service_tier_fallback"): logger.info("Used standard service tier after Flex Processing tier was unavailable") # Process the model's response if model_response.content: raw_text = model_response.content # Parse response to check for clarification requests or format output # Pass model info for conversation tracking model_info = {"provider": provider, "model_name": model_name, "model_response": model_response} # Get file references if they were stored file_references = getattr(self, "_current_file_references", None) tool_output = self._parse_response(raw_text, request, model_info, file_references) logger.info(f"✅ {self.name} tool completed successfully") else: # Handle cases where the model couldn't generate a response # This might happen due to safety filters or other constraints finish_reason = model_response.metadata.get("finish_reason", "Unknown") logger.warning(f"Response blocked or incomplete for {self.name}. Finish reason: {finish_reason}") tool_output = ToolOutput( status="error", content=f"Response blocked or incomplete. Finish reason: {finish_reason}", content_type="text", ) # Return standardized JSON response for consistent client handling return [TextContent(type="text", text=tool_output.model_dump_json())] except Exception as e: # Catch all exceptions to prevent server crashes # Return error information in standardized format logger = logging.getLogger(f"tools.{self.name}") error_msg = str(e) # Check if this is an MCP size check error from prepare_prompt if error_msg.startswith("MCP_SIZE_CHECK:"): logger.info(f"MCP prompt size limit exceeded in {self.name}") tool_output_json = error_msg[15:] # Remove "MCP_SIZE_CHECK:" prefix return [TextContent(type="text", text=tool_output_json)] # Check if this is a 500 INTERNAL error that asks for retry if "500 INTERNAL" in error_msg and "Please retry" in error_msg: logger.warning(f"500 INTERNAL error in {self.name} - attempting retry") try: # Single retry attempt using provider retry_response = provider.generate_content( prompt=prompt, model_name=model_name, system_prompt=system_prompt, temperature=temperature, thinking_mode=thinking_mode if provider.supports_thinking_mode(model_name) else None, images=images, ) if retry_response.content: # If successful, process normally retry_model_info = { "provider": provider, "model_name": model_name, "model_response": retry_response, } tool_output = self._parse_response(retry_response.content, request, retry_model_info) return [TextContent(type="text", text=tool_output.model_dump_json())] except Exception as retry_e: logger.error(f"Retry failed for {self.name} tool: {str(retry_e)}") error_msg = f"Tool failed after retry: {str(retry_e)}" logger.error(f"Error in {self.name} tool execution: {error_msg}", exc_info=True) error_output = ToolOutput( status="error", content=f"Error in {self.name}: {error_msg}", content_type="text", ) return [TextContent(type="text", text=error_output.model_dump_json())] def _parse_response( self, raw_text: str, request, model_info: Optional[dict] = None, file_references: Optional[list[FileReference]] = None, ) -> ToolOutput: """ Parse the raw response and check for clarification requests. This method formats the response and always offers a continuation opportunity unless max conversation turns have been reached. Args: raw_text: The raw text response from the model request: The original request for context model_info: Optional dict with model metadata file_references: Optional list of FileReference objects for summary/reference modes Returns: ToolOutput: Standardized output object """ logger = logging.getLogger(f"tools.{self.name}") try: # Try to parse as JSON to check for special status requests potential_json = json.loads(raw_text.strip()) if isinstance(potential_json, dict) and "status" in potential_json: status_key = potential_json.get("status") status_model = SPECIAL_STATUS_MODELS.get(status_key) if status_model: try: # Use Pydantic for robust validation of the special status parsed_status = status_model.model_validate(potential_json) logger.debug(f"{self.name} tool detected special status: {status_key}") # Extract model information for metadata metadata = { "original_request": ( request.model_dump() if hasattr(request, "model_dump") else str(request) ) } if model_info: model_name = model_info.get("model_name") if model_name: metadata["model_used"] = model_name return ToolOutput( status=status_key, content=parsed_status.model_dump_json(), content_type="json", metadata=metadata, ) except Exception as e: # Invalid payload for known status, log warning and continue as normal response logger.warning(f"Invalid {status_key} payload: {e}") except (json.JSONDecodeError, ValueError, TypeError): # Not a JSON special status request, treat as normal response pass # Normal text response - format using tool-specific formatting formatted_content = self.format_response(raw_text, request, model_info) # Always check if we should offer Claude a continuation opportunity continuation_offer = self._check_continuation_opportunity(request) if continuation_offer: logger.debug( f"Creating continuation offer for {self.name} with {continuation_offer['remaining_turns']} turns remaining" ) return self._create_continuation_offer_response( formatted_content, continuation_offer, request, model_info, file_references ) else: logger.debug(f"No continuation offer created for {self.name} - max turns reached") # If this is a threaded conversation (has continuation_id), save the response continuation_id = getattr(request, "continuation_id", None) if continuation_id: request_files = getattr(request, "files", []) or [] # Extract model metadata for conversation tracking model_provider = None model_name = None model_metadata = None if model_info: provider = model_info.get("provider") if provider: model_provider = provider.get_provider_type().value model_name = model_info.get("model_name") model_response = model_info.get("model_response") if model_response: model_metadata = {"usage": model_response.usage, "metadata": model_response.metadata} success = add_turn( continuation_id, "assistant", formatted_content, files=request_files, tool_name=self.name, model_provider=model_provider, model_name=model_name, model_metadata=model_metadata, ) if not success: logging.warning(f"Failed to add turn to thread {continuation_id} for {self.name}") # Determine content type based on the formatted content content_type = ( "markdown" if any(marker in formatted_content for marker in ["##", "**", "`", "- ", "1. "]) else "text" ) # Extract model information for metadata metadata = {"tool_name": self.name} if model_info: model_name = model_info.get("model_name") if model_name: metadata["model_used"] = model_name # Add file references if in summary/reference mode file_refs_for_output = None if file_references: file_refs_for_output = [ref.to_dict() for ref in file_references] return ToolOutput( status="success", content=formatted_content, content_type=content_type, metadata=metadata, file_references=file_refs_for_output, ) def _check_continuation_opportunity(self, request) -> Optional[dict]: """ Check if we should offer Claude a continuation opportunity. This is called when Gemini doesn't ask a follow-up question, but we want to give Claude the chance to continue the conversation if needed. Args: request: The original request Returns: Dict with continuation data if opportunity should be offered, None otherwise """ # Skip continuation offers in test mode import os if os.getenv("PYTEST_CURRENT_TEST"): return None continuation_id = getattr(request, "continuation_id", None) try: if continuation_id: # Check remaining turns in thread chain from utils.conversation_memory import get_thread_chain chain = get_thread_chain(continuation_id) if chain: # Count total turns across all threads in chain total_turns = sum(len(thread.turns) for thread in chain) remaining_turns = MAX_CONVERSATION_TURNS - total_turns - 1 # -1 for this response else: # Thread not found, don't offer continuation return None else: # New conversation, we have MAX_CONVERSATION_TURNS - 1 remaining # (since this response will be turn 1) remaining_turns = MAX_CONVERSATION_TURNS - 1 if remaining_turns <= 0: return None # Offer continuation opportunity return {"remaining_turns": remaining_turns, "tool_name": self.name} except Exception: # If anything fails, don't offer continuation return None def _create_continuation_offer_response( self, content: str, continuation_data: dict, request, model_info: Optional[dict] = None, file_references: Optional[list[FileReference]] = None, ) -> ToolOutput: """ Create a response offering Claude the opportunity to continue conversation. Args: content: The main response content continuation_data: Dict containing remaining_turns and tool_name request: Original request for context model_info: Optional dict with model metadata file_references: Optional list of FileReference objects Returns: ToolOutput configured with continuation offer """ try: # Create new thread for potential continuation (with parent link if continuing) continuation_id = getattr(request, "continuation_id", None) thread_id = create_thread( tool_name=self.name, initial_request=request.model_dump() if hasattr(request, "model_dump") else {}, parent_thread_id=continuation_id, # Link to parent if this is a continuation ) # Add this response as the first turn (assistant turn) # Use actually processed files from file preparation instead of original request files # This ensures directories are tracked as their individual expanded files request_files = getattr(self, "_actually_processed_files", []) or getattr(request, "files", []) or [] # Extract model metadata model_provider = None model_name = None model_metadata = None if model_info: provider = model_info.get("provider") if provider: model_provider = provider.get_provider_type().value model_name = model_info.get("model_name") model_response = model_info.get("model_response") if model_response: model_metadata = {"usage": model_response.usage, "metadata": model_response.metadata} add_turn( thread_id, "assistant", content, files=request_files, tool_name=self.name, model_provider=model_provider, model_name=model_name, model_metadata=model_metadata, ) # Create continuation offer remaining_turns = continuation_data["remaining_turns"] continuation_offer = ContinuationOffer( continuation_id=thread_id, note=( f"If you'd like to continue this discussion or need to provide me with further details or context, " f"you can use the continuation_id '{thread_id}' with any tool and any model. " f"You have {remaining_turns} more exchange(s) available in this conversation thread." ), suggested_tool_params={ "continuation_id": thread_id, "prompt": "[Your follow-up question, additional context, or further details]", }, remaining_turns=remaining_turns, ) # Extract model information for metadata metadata = {"tool_name": self.name, "thread_id": thread_id, "remaining_turns": remaining_turns} if model_info: model_name = model_info.get("model_name") if model_name: metadata["model_used"] = model_name # Add file references if in summary/reference mode file_refs_for_output = None if file_references: file_refs_for_output = [ref.to_dict() for ref in file_references] return ToolOutput( status="continuation_available", content=content, content_type="markdown", continuation_offer=continuation_offer, metadata=metadata, file_references=file_refs_for_output, ) except Exception as e: # If threading fails, return normal response but log the error logger = logging.getLogger(f"tools.{self.name}") logger.warning(f"Conversation threading failed in {self.name}: {str(e)}") # Extract model information for metadata metadata = {"tool_name": self.name, "threading_error": str(e)} if model_info: model_name = model_info.get("model_name") if model_name: metadata["model_used"] = model_name return ToolOutput( status="success", content=content, content_type="markdown", metadata=metadata, ) @abstractmethod async def prepare_prompt(self, request) -> str: """ Prepare the complete prompt for the Gemini model. This method should combine the system prompt with the user's request and any additional context (like file contents) needed for the task. Args: request: The validated request object Returns: str: Complete prompt ready for the model """ pass def format_response(self, response: str, request, model_info: Optional[dict] = None) -> str: """ Format the model's response for display. Override this method to add tool-specific formatting like headers, summaries, or structured output. Default implementation returns the response unchanged. Args: response: The raw response from the model request: The original request for context model_info: Optional dict with model metadata (provider, model_name, model_response) Returns: str: Formatted response """ return response def _validate_token_limit(self, text: str, context_type: str = "Context", context_window: Optional[int] = None) -> None: """ Validate token limit and raise ValueError if exceeded. This centralizes the token limit check that was previously duplicated in all prepare_prompt methods across tools. Args: text: The text to check context_type: Description of what's being checked (for error message) context_window: The model's context window size (uses model's actual limit if None) Raises: ValueError: If text exceeds context_window """ # Use the model's actual context window if not specified if context_window is None: if hasattr(self, '_model_context') and self._model_context: # Get the actual model's context window context_window = self._model_context.capabilities.context_window else: # Fallback to a reasonable default for modern models context_window = 1_000_000 # 1M tokens for Gemini-class models within_limit, estimated_tokens = check_token_limit(text, context_window) if not within_limit: raise ValueError( f"{context_type} too large (~{estimated_tokens:,} tokens). Maximum is {context_window:,} tokens." ) def _validate_and_correct_temperature(self, model_name: str, temperature: float) -> tuple[float, list[str]]: """ Validate and correct temperature for the specified model. Args: model_name: Name of the model to validate temperature for temperature: Temperature value to validate Returns: Tuple of (corrected_temperature, warning_messages) """ try: provider = self.get_model_provider(model_name) capabilities = provider.get_capabilities(model_name) constraint = capabilities.temperature_constraint warnings = [] if not constraint.validate(temperature): corrected = constraint.get_corrected_value(temperature) warning = ( f"Temperature {temperature} invalid for {model_name}. " f"{constraint.get_description()}. Using {corrected} instead." ) warnings.append(warning) return corrected, warnings return temperature, warnings except Exception as e: # If validation fails for any reason, use the original temperature # and log a warning (but don't fail the request) logger = logging.getLogger(f"tools.{self.name}") logger.warning(f"Temperature validation failed for {model_name}: {e}") return temperature, [f"Temperature validation failed: {e}"] def get_model_provider(self, model_name: str) -> ModelProvider: """ Get a model provider for the specified model. Args: model_name: Name of the model to use (can be provider-specific or generic) Returns: ModelProvider instance configured for the model Raises: ValueError: If no provider supports the requested model """ # Get provider from registry provider = ModelProviderRegistry.get_provider_for_model(model_name) if not provider: # Try to determine provider from model name patterns if "gemini" in model_name.lower() or model_name.lower() in ["flash", "pro"]: # Register Gemini provider if not already registered from providers.base import ProviderType from providers.gemini import GeminiModelProvider ModelProviderRegistry.register_provider(ProviderType.GOOGLE, GeminiModelProvider) provider = ModelProviderRegistry.get_provider(ProviderType.GOOGLE) elif "gpt" in model_name.lower() or "o3" in model_name.lower(): # Register OpenAI provider if not already registered from providers.base import ProviderType from providers.openai_provider import OpenAIModelProvider ModelProviderRegistry.register_provider(ProviderType.OPENAI, OpenAIModelProvider) provider = ModelProviderRegistry.get_provider(ProviderType.OPENAI) if not provider: raise ValueError( f"No provider found for model '{model_name}'. " f"Ensure the appropriate API key is set and the model name is correct." ) return provider