refactor: rename review tools for clarity and consistency
- Renamed `review_code` tool to `codereview` for better naming convention - Renamed `review_changes` tool to `precommit` to better reflect its purpose - Updated all tool descriptions to remove "Triggers:" sections and improve clarity - Updated all imports and references throughout the codebase - Renamed test files to match new tool names - Updated server.py tool registrations - All existing functionality preserved with improved naming This refactoring improves code organization and makes tool purposes clearer. 🤖 Generated with [Claude Code](https://claude.ai/code) Co-Authored-By: Claude <noreply@anthropic.com>
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Fahad
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tools/codereview.py
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tools/codereview.py
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"""
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Code Review tool - Comprehensive code analysis and review
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This tool provides professional-grade code review capabilities using
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Gemini's understanding of code patterns, best practices, and common issues.
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It can analyze individual files or entire codebases, providing actionable
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feedback categorized by severity.
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Key Features:
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- Multi-file and directory support
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- Configurable review types (full, security, performance, quick)
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- Severity-based issue filtering
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- Custom focus areas and coding standards
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- Structured output with specific remediation steps
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"""
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from typing import Any, Optional
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from mcp.types import TextContent
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from pydantic import Field
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from config import TEMPERATURE_ANALYTICAL
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from prompts import CODEREVIEW_PROMPT
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from utils import read_files
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from .base import BaseTool, ToolRequest
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from .models import ToolOutput
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class CodeReviewRequest(ToolRequest):
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"""
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Request model for the code review tool.
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This model defines all parameters that can be used to customize
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the code review process, from selecting files to specifying
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review focus and standards.
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"""
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files: list[str] = Field(
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...,
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description="Code files or directories to review (must be absolute paths)",
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)
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context: str = Field(
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...,
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description="User's summary of what the code does, expected behavior, constraints, and review objectives",
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)
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review_type: str = Field("full", description="Type of review: full|security|performance|quick")
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focus_on: Optional[str] = Field(None, description="Specific aspects to focus on during review")
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standards: Optional[str] = Field(None, description="Coding standards or guidelines to enforce")
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severity_filter: str = Field(
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"all",
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description="Minimum severity to report: critical|high|medium|all",
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)
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class CodeReviewTool(BaseTool):
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"""
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Professional code review tool implementation.
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This tool analyzes code for bugs, security vulnerabilities, performance
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issues, and code quality problems. It provides detailed feedback with
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severity ratings and specific remediation steps.
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"""
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def get_name(self) -> str:
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return "codereview"
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def get_description(self) -> str:
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return (
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"PROFESSIONAL CODE REVIEW - Comprehensive analysis for bugs, security, and quality. "
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"Supports both individual files and entire directories/projects. "
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"Use this when you need to review code, check for issues, find bugs, or perform security audits. "
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"I'll identify issues by severity (Critical→High→Medium→Low) with specific fixes. "
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"Supports focused reviews: security, performance, or quick checks. "
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"Choose thinking_mode based on review scope: 'low' for small code snippets, "
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"'medium' for standard files/modules (default), 'high' for complex systems/architectures, "
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"'max' for critical security audits or large codebases requiring deepest analysis."
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)
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def get_input_schema(self) -> dict[str, Any]:
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return {
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"type": "object",
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"properties": {
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"files": {
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"type": "array",
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"items": {"type": "string"},
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"description": "Code files or directories to review (must be absolute paths)",
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},
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"context": {
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"type": "string",
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"description": "User's summary of what the code does, expected behavior, constraints, and review objectives",
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},
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"review_type": {
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"type": "string",
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"enum": ["full", "security", "performance", "quick"],
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"default": "full",
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"description": "Type of review to perform",
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},
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"focus_on": {
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"type": "string",
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"description": "Specific aspects to focus on",
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},
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"standards": {
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"type": "string",
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"description": "Coding standards to enforce",
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},
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"severity_filter": {
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"type": "string",
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"enum": ["critical", "high", "medium", "all"],
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"default": "all",
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"description": "Minimum severity level to report",
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},
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"temperature": {
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"type": "number",
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"description": "Temperature (0-1, default 0.2 for consistency)",
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"minimum": 0,
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"maximum": 1,
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},
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"thinking_mode": {
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"type": "string",
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"enum": ["minimal", "low", "medium", "high", "max"],
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"description": "Thinking depth: minimal (128), low (2048), medium (8192), high (16384), max (32768)",
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},
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"use_websearch": {
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"type": "boolean",
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"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.",
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"default": False,
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},
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},
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"required": ["files", "context"],
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}
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def get_system_prompt(self) -> str:
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return CODEREVIEW_PROMPT
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def get_default_temperature(self) -> float:
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return TEMPERATURE_ANALYTICAL
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def get_request_model(self):
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return CodeReviewRequest
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async def execute(self, arguments: dict[str, Any]) -> list[TextContent]:
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"""Override execute to check focus_on size before processing"""
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# First validate request
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request_model = self.get_request_model()
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request = request_model(**arguments)
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# Check focus_on size if provided
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if request.focus_on:
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size_check = self.check_prompt_size(request.focus_on)
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if size_check:
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return [TextContent(type="text", text=ToolOutput(**size_check).model_dump_json())]
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# Continue with normal execution
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return await super().execute(arguments)
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async def prepare_prompt(self, request: CodeReviewRequest) -> str:
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"""
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Prepare the code review prompt with customized instructions.
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This method reads the requested files, validates token limits,
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and constructs a detailed prompt based on the review parameters.
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Args:
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request: The validated review request
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Returns:
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str: Complete prompt for the Gemini model
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Raises:
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ValueError: If the code exceeds token limits
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"""
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# Check for prompt.txt in files
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prompt_content, updated_files = self.handle_prompt_file(request.files)
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# If prompt.txt was found, use it as focus_on
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if prompt_content:
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request.focus_on = prompt_content
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# Update request files list
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if updated_files is not None:
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request.files = updated_files
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# Read all requested files, expanding directories as needed
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file_content = read_files(request.files)
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# Validate that the code fits within model context limits
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self._validate_token_limit(file_content, "Code")
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# Build customized review instructions based on review type
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review_focus = []
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if request.review_type == "security":
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review_focus.append("Focus on security vulnerabilities and authentication issues")
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elif request.review_type == "performance":
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review_focus.append("Focus on performance bottlenecks and optimization opportunities")
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elif request.review_type == "quick":
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review_focus.append("Provide a quick review focusing on critical issues only")
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# Add any additional focus areas specified by the user
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if request.focus_on:
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review_focus.append(f"Pay special attention to: {request.focus_on}")
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# Include custom coding standards if provided
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if request.standards:
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review_focus.append(f"Enforce these standards: {request.standards}")
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# Apply severity filtering to reduce noise if requested
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if request.severity_filter != "all":
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review_focus.append(f"Only report issues of {request.severity_filter} severity or higher")
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focus_instruction = "\n".join(review_focus) if review_focus else ""
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# Add web search instruction if enabled
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websearch_instruction = self.get_websearch_instruction(
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request.use_websearch,
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"""Specifically search for:
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- Security vulnerabilities and CVEs for libraries/frameworks used
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- Best practices for the languages and frameworks in the code
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- Common anti-patterns and their solutions
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- Performance optimization techniques
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- Recent updates or deprecations in APIs used""",
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)
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# Construct the complete prompt with system instructions and code
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full_prompt = f"""{self.get_system_prompt()}{websearch_instruction}
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=== USER CONTEXT ===
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{request.context}
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=== END CONTEXT ===
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{focus_instruction}
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=== CODE TO REVIEW ===
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{file_content}
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=== END CODE ===
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Please provide a code review aligned with the user's context and expectations, following the format specified in the system prompt."""
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return full_prompt
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def format_response(self, response: str, request: CodeReviewRequest) -> str:
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"""
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Format the review response with appropriate headers.
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Adds context about the review type and focus area to help
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users understand the scope of the review.
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Args:
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response: The raw review from the model
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request: The original request for context
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Returns:
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str: Formatted response with headers
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"""
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header = f"Code Review ({request.review_type.upper()})"
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if request.focus_on:
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header += f" - Focus: {request.focus_on}"
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return f"""{header}
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{'=' * 50}
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{response}
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---
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**Claude's Next Steps:**
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1. **Understand the Context**: First examine the specific functions, files, and code sections mentioned in the review to understand each issue thoroughly.
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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.
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3. **Implement Selected Fixes**: Only implement the fixes the user chooses, ensuring each change is made correctly and maintains code quality.
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Remember: Always understand the code context before suggesting fixes, and let the user decide which improvements to implement."""
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