fix: Docker path translation for review_changes and code deduplication

- Fixed review_changes tool to properly translate host paths to container paths in Docker
- Prevents "No such file or directory" errors when running in Docker containers
- Added proper error handling with clear messages when paths are inaccessible

refactor: Centralized token limit validation across all tools
- Added _validate_token_limit method to BaseTool to eliminate code duplication
- Reduced ~25 lines of duplicated code across 5 tools (analyze, chat, debug_issue, review_code, think_deeper)
- Maintains exact same error messages and behavior

feat: Enhanced large prompt handling
- Added support for prompts >50K chars by requesting file-based input
- Preserves MCP's ~25K token capacity for responses
- All tools now check prompt size before processing

test: Added comprehensive Docker path integration tests
- Tests for path translation, security validation, and error handling
- Tests for review_changes tool specifically with Docker paths
- Fixed failing think_deeper test (updated default from "max" to "high")

chore: Code quality improvements
- Applied black formatting across all files
- Fixed import sorting with isort
- All tests passing (96 tests)
- Standardized error handling follows MCP TextContent format

The changes ensure consistent behavior across all environments while reducing code duplication and improving maintainability.

🤖 Generated with [Claude Code](https://claude.ai/code)

Co-Authored-By: Claude <noreply@anthropic.com>

tools/models.py

@@ -2,14 +2,17 @@
 Data models for tool responses and interactions
 """
 
+from typing import Any, Dict, List, Literal, Optional
+
 from pydantic import BaseModel, Field
-from typing import Literal, Optional, Dict, Any, List
 
 
 class ToolOutput(BaseModel):
     """Standardized output format for all tools"""
 
-    status: Literal["success", "error", "requires_clarification"] = "success"
+    status: Literal[
+        "success", "error", "requires_clarification", "requires_file_prompt"
+    ] = "success"
     content: str = Field(..., description="The main content/response from the tool")
     content_type: Literal["text", "markdown", "json"] = "text"
     metadata: Optional[Dict[str, Any]] = Field(default_factory=dict)