""" Code Review tool - Comprehensive code analysis and review This tool provides professional-grade code review capabilities using the chosen model's understanding of code patterns, best practices, and common issues. It can analyze individual files or entire codebases, providing actionable feedback categorized by severity. Key Features: - Multi-file and directory support - Configurable review types (full, security, performance, quick) - Severity-based issue filtering - Custom focus areas and coding standards - Structured output with specific remediation steps """ from typing import Any, Optional from pydantic import Field from config import TEMPERATURE_ANALYTICAL from systemprompts import CODEREVIEW_PROMPT from .base import BaseTool, ToolRequest class CodeReviewRequest(ToolRequest): """ Request model for the code review tool. This model defines all parameters that can be used to customize the code review process, from selecting files to specifying review focus and standards. """ files: list[str] = Field( ..., description="Code files or directories to review (must be absolute paths)", ) prompt: str = Field( ..., description="User's summary of what the code does, expected behavior, constraints, and review objectives", ) review_type: str = Field("full", description="Type of review: full|security|performance|quick") focus_on: Optional[str] = Field( None, description="Specific aspects to focus on, or additional context that would help understand areas of concern", ) standards: Optional[str] = Field(None, description="Coding standards or guidelines to enforce") severity_filter: str = Field( "all", description="Minimum severity to report: critical|high|medium|all", ) class CodeReviewTool(BaseTool): """ Professional code review tool implementation. This tool analyzes code for bugs, security vulnerabilities, performance issues, and code quality problems. It provides detailed feedback with severity ratings and specific remediation steps. """ def get_name(self) -> str: return "codereview" def get_description(self) -> str: return ( "CODE REVIEW - Bug, security & quality analysis. " "Files or directories. Issues by severity with fixes. " "Review types: full/security/performance/quick." ) def get_input_schema(self) -> dict[str, Any]: schema = { "type": "object", "properties": { "files": { "type": "array", "items": {"type": "string"}, "description": "Code files or directories to review (must be absolute paths)", }, "model": self.get_model_field_schema(), "prompt": { "type": "string", "description": "User's summary of what the code does, expected behavior, constraints, and review objectives", }, "review_type": { "type": "string", "enum": ["full", "security", "performance", "quick"], "default": "full", "description": "Type of review to perform", }, "focus_on": { "type": "string", "description": "Specific aspects to focus on, or additional context that would help understand areas of concern", }, "standards": { "type": "string", "description": "Coding standards to enforce", }, "severity_filter": { "type": "string", "enum": ["critical", "high", "medium", "all"], "default": "all", "description": "Minimum severity level to report", }, "temperature": { "type": "number", "description": "Temperature (0-1, default 0.2 for consistency)", "minimum": 0, "maximum": 1, }, "thinking_mode": { "type": "string", "enum": ["minimal", "low", "medium", "high", "max"], "description": "Thinking depth: minimal (0.5% of model max), low (8%), medium (33%), high (67%), max (100% of model max)", }, "use_websearch": { "type": "boolean", "description": "Enable web search for documentation, best practices, and current information. 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.", "default": True, }, "file_handling_mode": { "type": "string", "enum": ["embedded", "summary", "reference"], "default": "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.", }, "continuation_id": { "type": "string", "description": "Thread continuation ID for multi-turn conversations. Can be used to continue conversations across different tools. Only provide this if continuing a previous conversation thread.", }, }, "required": ["files", "prompt"] + (["model"] if self.is_effective_auto_mode() else []), } return schema def get_system_prompt(self) -> str: return CODEREVIEW_PROMPT def get_default_temperature(self) -> float: return TEMPERATURE_ANALYTICAL # Line numbers are enabled by default from base class for precise feedback def get_request_model(self): return CodeReviewRequest async def prepare_prompt(self, request: CodeReviewRequest) -> str: """ Prepare the code review prompt with customized instructions. This method reads the requested files, validates token limits, and constructs a detailed prompt based on the review parameters. Args: request: The validated review request Returns: str: Complete prompt for the model Raises: ValueError: If the code exceeds token limits """ # Check for prompt.txt in files prompt_content, updated_files = self.handle_prompt_file(request.files) # If prompt.txt was found, incorporate it into the prompt if prompt_content: request.prompt = prompt_content + "\n\n" + request.prompt # Update request files list if updated_files is not None: request.files = updated_files # Check user input size at MCP transport boundary (before adding internal content) user_content = request.prompt size_check = self.check_prompt_size(user_content) if size_check: from tools.models import ToolOutput raise ValueError(f"MCP_SIZE_CHECK:{ToolOutput(**size_check).model_dump_json()}") # Also check focus_on field if provided (user input) if request.focus_on: focus_size_check = self.check_prompt_size(request.focus_on) if focus_size_check: from tools.models import ToolOutput raise ValueError(f"MCP_SIZE_CHECK:{ToolOutput(**focus_size_check).model_dump_json()}") # Use centralized file processing logic continuation_id = getattr(request, "continuation_id", None) file_content, processed_files, file_references = self._prepare_file_content_for_prompt( request.files, continuation_id, "Code" ) # Store file references for response formatting if file_references: self._store_file_references(file_references) self._actually_processed_files = processed_files # Build customized review instructions based on review type review_focus = [] if request.review_type == "security": review_focus.append("Focus on security vulnerabilities and authentication issues") elif request.review_type == "performance": review_focus.append("Focus on performance bottlenecks and optimization opportunities") elif request.review_type == "quick": review_focus.append("Provide a quick review focusing on critical issues only") # Add any additional focus areas specified by the user if request.focus_on: review_focus.append(f"Pay special attention to: {request.focus_on}") # Include custom coding standards if provided if request.standards: review_focus.append(f"Enforce these standards: {request.standards}") # Apply severity filtering to reduce noise if requested if request.severity_filter != "all": review_focus.append(f"Only report issues of {request.severity_filter} severity or higher") focus_instruction = "\n".join(review_focus) if review_focus else "" # Add web search instruction if enabled websearch_instruction = self.get_websearch_instruction( request.use_websearch, """When reviewing code, consider if searches for these would help: - Security vulnerabilities and CVEs for libraries/frameworks used - Best practices for the languages and frameworks in the code - Common anti-patterns and their solutions - Performance optimization techniques - Recent updates or deprecations in APIs used""", ) # Construct the complete prompt with system instructions and code full_prompt = f"""{self.get_system_prompt()}{websearch_instruction} === USER CONTEXT === {request.prompt} === END CONTEXT === {focus_instruction} === CODE TO REVIEW === {file_content} === END CODE === Please provide a code review aligned with the user's context and expectations, following the format specified in the system prompt.""" return full_prompt def format_response(self, response: str, request: CodeReviewRequest, model_info: Optional[dict] = None) -> str: """ Format the review response. Args: response: The raw review from the model request: The original request for context model_info: Optional dict with model metadata Returns: str: Formatted response with next steps """ return f"""{response} --- **Claude's Next Steps:** 1. **Understand the Context**: First examine the specific functions, files, and code sections mentioned in the review to understand each issue thoroughly. 2. **Present Options to User**: After understanding the issues, ask the user which specific improvements they would like to implement, presenting them as a clear list of options. 3. **Implement Selected Fixes**: Only implement the fixes the user chooses, ensuring each change is made correctly and maintains code quality. Remember: Always understand the code context before suggesting fixes, and let the user decide which improvements to implement."""