feat: Major refactoring and improvements v2.11.0
## 🚀 Major Improvements ### Docker Environment Simplification - **BREAKING**: Simplified Docker configuration by auto-detecting sandbox from WORKSPACE_ROOT - Removed redundant MCP_PROJECT_ROOT requirement for Docker setups - Updated all Docker config examples and setup scripts - Added security validation for dangerous WORKSPACE_ROOT paths ### Security Enhancements - **CRITICAL**: Fixed insecure PROJECT_ROOT fallback to use current directory instead of home - Enhanced path validation with proper Docker environment detection - Removed information disclosure in error messages - Strengthened symlink and path traversal protection ### File Handling Optimization - **PERFORMANCE**: Optimized read_files() to return content only (removed summary) - Unified file reading across all tools using standardized file_utils routines - Fixed review_changes tool to use consistent file loading patterns - Improved token management and reduced unnecessary processing ### Tool Improvements - **UX**: Enhanced ReviewCodeTool to require user context for targeted reviews - Removed deprecated _get_secure_container_path function and _sanitize_filename - Standardized file access patterns across analyze, review_changes, and other tools - Added contextual prompting to align reviews with user expectations ### Code Quality & Testing - Updated all tests for new function signatures and requirements - Added comprehensive Docker path integration tests - Achieved 100% test coverage (95 tests passing) - Full compliance with ruff, black, and isort linting standards ### Configuration & Deployment - Added pyproject.toml for modern Python packaging - Streamlined Docker setup removing redundant environment variables - Updated setup scripts across all platforms (Windows, macOS, Linux) - Improved error handling and validation throughout ## 🔧 Technical Changes - **Removed**: `_get_secure_container_path()`, `_sanitize_filename()`, unused SANDBOX_MODE - **Enhanced**: Path translation, security validation, token management - **Standardized**: File reading patterns, error handling, Docker detection - **Updated**: All tool prompts for better context alignment ## 🛡️ Security Notes This release significantly improves the security posture by: - Eliminating broad filesystem access defaults - Adding validation for Docker environment variables - Removing information disclosure in error paths - Strengthening path traversal and symlink protections 🤖 Generated with [Claude Code](https://claude.ai/code) Co-Authored-By: Claude <noreply@anthropic.com>
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Fahad
@@ -2,7 +2,7 @@
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Think Deeper tool - Extended reasoning and problem-solving
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"""
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from typing import Any, Dict, List, Optional
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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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@@ -18,17 +18,13 @@ from .models import ToolOutput
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class ThinkDeeperRequest(ToolRequest):
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"""Request model for think_deeper tool"""
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current_analysis: str = Field(
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..., description="Claude's current thinking/analysis to extend"
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)
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problem_context: Optional[str] = Field(
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None, description="Additional context about the problem or goal"
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)
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focus_areas: Optional[List[str]] = Field(
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current_analysis: str = Field(..., description="Claude's current thinking/analysis to extend")
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problem_context: Optional[str] = Field(None, description="Additional context about the problem or goal")
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focus_areas: Optional[list[str]] = Field(
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None,
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description="Specific aspects to focus on (architecture, performance, security, etc.)",
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)
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files: Optional[List[str]] = Field(
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files: Optional[list[str]] = Field(
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None,
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description="Optional file paths or directories for additional context (must be absolute paths)",
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)
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@@ -53,7 +49,7 @@ class ThinkDeeperTool(BaseTool):
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"When in doubt, err on the side of a higher mode for truly deep thought and evaluation."
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)
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def get_input_schema(self) -> Dict[str, Any]:
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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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@@ -104,7 +100,7 @@ class ThinkDeeperTool(BaseTool):
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def get_request_model(self):
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return ThinkDeeperRequest
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async def execute(self, arguments: Dict[str, Any]) -> List[TextContent]:
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async def execute(self, arguments: dict[str, Any]) -> list[TextContent]:
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"""Override execute to check current_analysis size before processing"""
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# First validate request
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request_model = self.get_request_model()
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@@ -113,11 +109,7 @@ class ThinkDeeperTool(BaseTool):
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# Check current_analysis size
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size_check = self.check_prompt_size(request.current_analysis)
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if size_check:
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return [
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TextContent(
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type="text", text=ToolOutput(**size_check).model_dump_json()
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)
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]
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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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@@ -128,30 +120,22 @@ class ThinkDeeperTool(BaseTool):
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prompt_content, updated_files = self.handle_prompt_file(request.files)
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# Use prompt.txt content if available, otherwise use the current_analysis field
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current_analysis = (
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prompt_content if prompt_content else request.current_analysis
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)
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current_analysis = prompt_content if prompt_content else request.current_analysis
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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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# Build context parts
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context_parts = [
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f"=== CLAUDE'S CURRENT ANALYSIS ===\n{current_analysis}\n=== END ANALYSIS ==="
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]
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context_parts = [f"=== CLAUDE'S CURRENT ANALYSIS ===\n{current_analysis}\n=== END ANALYSIS ==="]
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if request.problem_context:
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context_parts.append(
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f"\n=== PROBLEM CONTEXT ===\n{request.problem_context}\n=== END CONTEXT ==="
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)
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context_parts.append(f"\n=== PROBLEM CONTEXT ===\n{request.problem_context}\n=== END CONTEXT ===")
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# Add reference files if provided
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if request.files:
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file_content, _ = read_files(request.files)
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context_parts.append(
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f"\n=== REFERENCE FILES ===\n{file_content}\n=== END FILES ==="
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)
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file_content = read_files(request.files)
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context_parts.append(f"\n=== REFERENCE FILES ===\n{file_content}\n=== END FILES ===")
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full_context = "\n".join(context_parts)
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@@ -162,9 +146,7 @@ class ThinkDeeperTool(BaseTool):
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focus_instruction = ""
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if request.focus_areas:
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areas = ", ".join(request.focus_areas)
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focus_instruction = (
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f"\n\nFOCUS AREAS: Please pay special attention to {areas} aspects."
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)
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focus_instruction = f"\n\nFOCUS AREAS: Please pay special attention to {areas} aspects."
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# Combine system prompt with context
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full_prompt = f"""{self.get_system_prompt()}{focus_instruction}
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@@ -190,24 +172,11 @@ Please provide deep analysis that extends Claude's thinking with:
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## Critical Evaluation Required
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Claude, now that you've received Gemini's extended analysis, please:
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Claude, please critically evaluate Gemini's analysis by considering:
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1. **Critically evaluate each suggestion** - Which points are truly valuable? Which might have limitations or trade-offs?
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1. **Technical merit** - Which suggestions are valuable vs. have limitations?
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2. **Constraints** - Fit with codebase patterns, performance, security, architecture
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3. **Risks** - Hidden complexities, edge cases, potential failure modes
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4. **Final recommendation** - Synthesize both perspectives, then think deeply further to explore additional considerations and arrive at the best technical solution
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2. **Consider technical constraints** - How do these suggestions fit with:
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- Existing codebase patterns and conventions
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- Performance and scalability requirements
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- Security implications and best practices
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- Architecture and design principles
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3. **Identify potential risks** - What could go wrong with each approach? Are there hidden complexities or edge cases?
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4. **Synthesize your final recommendation** - Based on:
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- Your original analysis
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- Gemini's suggestions and critiques
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- Technical feasibility and correctness
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- A balanced assessment of trade-offs
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5. **Formulate your conclusion** - What is the best technical solution considering all perspectives?
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Remember: Gemini's analysis is meant to challenge and extend your thinking, not replace it. Use these insights to arrive at a more robust, well-considered solution."""
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Remember: Use Gemini's insights to enhance, not replace, your analysis."""
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