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feat: Major refactoring and improvements v2.11.0

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## 🚀 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>
This commit is contained in:
Fahad
2025-06-10 09:50:05 +04:00
parent 7ea790ef88
commit 27add4d05d
34 changed files with 593 additions and 759 deletions
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95
tools/base.py
View File

@@ -16,7 +16,7 @@ Key responsibilities:
import json
import os
from abc import ABC, abstractmethod
from typing import Any, Dict, List, Literal, Optional
from typing import Any, Literal, Optional
from google import genai
from google.genai import types
@@ -24,7 +24,7 @@ from mcp.types import TextContent
from pydantic import BaseModel, Field
from config import MCP_PROMPT_SIZE_LIMIT
from utils.file_utils import read_file_content
from utils.file_utils import read_file_content, translate_path_for_environment
from .models import ClarificationRequest, ToolOutput
@@ -38,12 +38,8 @@ class ToolRequest(BaseModel):
these common fields.
"""
model: Optional[str] = Field(
None, description="Model to use (defaults to Gemini 2.5 Pro)"
)
temperature: Optional[float] = Field(
None, description="Temperature for response (tool-specific defaults)"
)
model: Optional[str] = Field(None, description="Model to use (defaults to Gemini 2.5 Pro)")
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(
@@ -100,7 +96,7 @@ class BaseTool(ABC):
pass
@abstractmethod
def get_input_schema(self) -> Dict[str, Any]:
def get_input_schema(self) -> dict[str, Any]:
"""
Return the JSON Schema that defines this tool's parameters.
@@ -197,7 +193,7 @@ class BaseTool(ABC):
return None
def check_prompt_size(self, text: str) -> Optional[Dict[str, Any]]:
def check_prompt_size(self, text: str) -> Optional[dict[str, Any]]:
"""
Check if a text field is too large for MCP's token limits.
@@ -231,9 +227,7 @@ class BaseTool(ABC):
}
return None
def handle_prompt_file(
self, files: Optional[List[str]]
) -> tuple[Optional[str], Optional[List[str]]]:
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.
@@ -245,7 +239,7 @@ class BaseTool(ABC):
mechanism to bypass token constraints while preserving response capacity.
Args:
files: List of file paths
files: List of file paths (will be translated for current environment)
Returns:
tuple: (prompt_content, updated_files_list)
@@ -257,21 +251,47 @@ class BaseTool(ABC):
updated_files = []
for file_path in files:
# Translate path for current environment (Docker/direct)
translated_path = translate_path_for_environment(file_path)
# 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":
if os.path.basename(translated_path) == "prompt.txt":
try:
prompt_content = read_file_content(file_path)
# Read prompt.txt content and extract just the text
content, _ = read_file_content(translated_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]:
async def execute(self, arguments: dict[str, Any]) -> list[TextContent]:
"""
Execute the tool with the provided arguments.
@@ -338,11 +358,7 @@ class BaseTool(ABC):
else:
# Handle cases where the model couldn't generate a response
# This might happen due to safety filters or other constraints
finish_reason = (
response.candidates[0].finish_reason
if response.candidates
else "Unknown"
)
finish_reason = response.candidates[0].finish_reason if response.candidates else "Unknown"
tool_output = ToolOutput(
status="error",
content=f"Response blocked or incomplete. Finish reason: {finish_reason}",
@@ -380,10 +396,7 @@ class BaseTool(ABC):
# Try to parse as JSON to check for clarification requests
potential_json = json.loads(raw_text.strip())
if (
isinstance(potential_json, dict)
and potential_json.get("status") == "requires_clarification"
):
if isinstance(potential_json, dict) and potential_json.get("status") == "requires_clarification":
# Validate the clarification request structure
clarification = ClarificationRequest(**potential_json)
return ToolOutput(
@@ -391,11 +404,7 @@ class BaseTool(ABC):
content=clarification.model_dump_json(),
content_type="json",
metadata={
"original_request": (
request.model_dump()
if hasattr(request, "model_dump")
else str(request)
)
"original_request": (request.model_dump() if hasattr(request, "model_dump") else str(request))
},
)
@@ -408,11 +417,7 @@ class BaseTool(ABC):
# Determine content type based on the formatted content
content_type = (
"markdown"
if any(
marker in formatted_content for marker in ["##", "**", "`", "- ", "1. "]
)
else "text"
"markdown" if any(marker in formatted_content for marker in ["##", "**", "`", "- ", "1. "]) else "text"
)
return ToolOutput(
@@ -479,9 +484,7 @@ class BaseTool(ABC):
f"Maximum is {MAX_CONTEXT_TOKENS:,} tokens."
)
def create_model(
self, model_name: str, temperature: float, thinking_mode: str = "medium"
):
def create_model(self, model_name: str, temperature: float, thinking_mode: str = "medium"):
"""
Create a configured Gemini model instance.
@@ -522,9 +525,7 @@ class BaseTool(ABC):
# Create a wrapper class to provide a consistent interface
# This abstracts the differences between API versions
class ModelWrapper:
def __init__(
self, client, model_name, temperature, thinking_budget
):
def __init__(self, client, model_name, temperature, thinking_budget):
self.client = client
self.model_name = model_name
self.temperature = temperature
@@ -537,9 +538,7 @@ class BaseTool(ABC):
config=types.GenerateContentConfig(
temperature=self.temperature,
candidate_count=1,
thinking_config=types.ThinkingConfig(
thinking_budget=self.thinking_budget
),
thinking_config=types.ThinkingConfig(thinking_budget=self.thinking_budget),
),
)
@@ -617,11 +616,7 @@ class BaseTool(ABC):
"content": type(
"obj",
(object,),
{
"parts": [
type("obj", (object,), {"text": text})
]
},
{"parts": [type("obj", (object,), {"text": text})]},
)(),
"finish_reason": "STOP",
},