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refactor: cleanup and comprehensive documentation

Browse Source 
Major changes:
- Add comprehensive documentation to all modules with detailed docstrings
- Remove unused THINKING_MODEL config (use single GEMINI_MODEL with thinking_mode param)
- Remove list_models functionality (simplified to single model configuration)
- Rename DEFAULT_MODEL to GEMINI_MODEL for clarity
- Remove unused python-dotenv dependency
- Fix missing pydantic in setup.py dependencies

Documentation improvements:
- Document security measures in file_utils.py (path validation, sandboxing)
- Add detailed comments to critical logic sections
- Document tool creation process in BaseTool
- Explain configuration values and their impact
- Add comprehensive function-level documentation

Code quality:
- Apply black formatting to all files
- Fix all ruff linting issues
- Update tests to match refactored code
- All 63 tests passing

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

Co-Authored-By: Claude <noreply@anthropic.com>
This commit is contained in:
Fahad
2025-06-09 19:04:24 +04:00
parent fd6e2f9b64
commit 783ba73181
12 changed files with 639 additions and 260 deletions
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14
README.md
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@@ -122,7 +122,6 @@ Just ask Claude naturally:
- **Pre-commit validation?** → `review_changes` (validate git changes before committing)
- **Something's broken?** → `debug_issue` (root cause analysis, error tracing)
- **Want to understand code?** → `analyze` (architecture, patterns, dependencies)
- **Check models?** → `list_models` (see available Gemini models)
- **Server info?** → `get_version` (version and configuration details)
**Tools Overview:**
@@ -132,8 +131,7 @@ Just ask Claude naturally:
4. [`review_changes`](#4-review_changes---pre-commit-validation) - Validate git changes before committing
5. [`debug_issue`](#5-debug_issue---expert-debugging-assistant) - Root cause analysis and debugging
6. [`analyze`](#6-analyze---smart-file-analysis) - General-purpose file and code analysis
7. [`list_models`](#7-list_models---see-available-gemini-models) - List available Gemini models
8. [`get_version`](#8-get_version---server-information) - Get server version and configuration
7. [`get_version`](#7-get_version---server-information) - Get server version and configuration
### 1. `chat` - General Development Chat & Collaborative Thinking
**Your thinking partner - bounce ideas, get second opinions, brainstorm collaboratively**
@@ -346,13 +344,7 @@ Combine your findings with gemini's to create a comprehensive security report."
**Triggers:** analyze, examine, look at, understand, inspect
### 7. `list_models` - See Available Gemini Models
```
"Use gemini to list available models"
"Get gemini to show me what models I can use"
```
### 8. `get_version` - Server Information
### 7. `get_version` - Server Information
```
"Use gemini for its version"
"Get gemini to show server configuration"
@@ -530,7 +522,7 @@ All tools support a `thinking_mode` parameter that controls Gemini's thinking bu
The server includes several configurable properties that control its behavior:
### Model Configuration
- **`DEFAULT_MODEL`**: `"gemini-2.5-pro-preview-06-05"` - The latest Gemini 2.5 Pro model with native thinking support
- **`GEMINI_MODEL`**: `"gemini-2.5-pro-preview-06-05"` - The latest Gemini 2.5 Pro model with native thinking support
- **`MAX_CONTEXT_TOKENS`**: `1,000,000` - Maximum input context (1M tokens for Gemini 2.5 Pro)
### Temperature Defaults

37
config.py
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@@ -1,20 +1,43 @@
"""
Configuration and constants for Gemini MCP Server
This module centralizes all configuration settings for the Gemini MCP Server.
It defines model configurations, token limits, temperature defaults, and other
constants used throughout the application.
Configuration values can be overridden by environment variables where appropriate.
"""
# Version and metadata
__version__ = "2.8.0"
__updated__ = "2025-09-09"
__author__ = "Fahad Gilani"
# These values are used in server responses and for tracking releases
__version__ = "2.8.0" # Semantic versioning: MAJOR.MINOR.PATCH
__updated__ = "2025-09-09" # Last update date in ISO format
__author__ = "Fahad Gilani" # Primary maintainer
# Model configuration
DEFAULT_MODEL = "gemini-2.5-pro-preview-06-05"
THINKING_MODEL = (
"gemini-2.0-flash-thinking-exp" # Enhanced reasoning model for think_deeper
)
# GEMINI_MODEL: The Gemini model used for all AI operations
# This should be a stable, high-performance model suitable for code analysis
GEMINI_MODEL = "gemini-2.5-pro-preview-06-05"
# MAX_CONTEXT_TOKENS: Maximum number of tokens that can be included in a single request
# This limit includes both the prompt and expected response
# Gemini Pro models support up to 1M tokens, but practical usage should reserve
# space for the model's response (typically 50K-100K tokens reserved)
MAX_CONTEXT_TOKENS = 1_000_000 # 1M tokens for Gemini Pro
# Temperature defaults for different tool types
# Temperature controls the randomness/creativity of model responses
# Lower values (0.0-0.3) produce more deterministic, focused responses
# Higher values (0.7-1.0) produce more creative, varied responses
# TEMPERATURE_ANALYTICAL: Used for tasks requiring precision and consistency
# Ideal for code review, debugging, and error analysis where accuracy is critical
TEMPERATURE_ANALYTICAL = 0.2 # For code review, debugging
# TEMPERATURE_BALANCED: Middle ground for general conversations
# Provides a good balance between consistency and helpful variety
TEMPERATURE_BALANCED = 0.5 # For general chat
# TEMPERATURE_CREATIVE: Higher temperature for exploratory tasks
# Used when brainstorming, exploring alternatives, or architectural discussions
TEMPERATURE_CREATIVE = 0.7 # For architecture, deep thinking

1
requirements.txt
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@@ -1,6 +1,5 @@
mcp>=1.0.0
google-genai>=1.19.0
python-dotenv>=1.0.0
pydantic>=2.0.0
# Development dependencies

197
server.py
View File

@@ -1,5 +1,21 @@
"""
Gemini MCP Server - Main server implementation
This module implements the core MCP (Model Context Protocol) server that provides
AI-powered tools for code analysis, review, and assistance using Google's Gemini models.
The server follows the MCP specification to expose various AI tools as callable functions
that can be used by MCP clients (like Claude). Each tool provides specialized functionality
such as code review, debugging, deep thinking, and general chat capabilities.
Key Components:
- MCP Server: Handles protocol communication and tool discovery
- Tool Registry: Maps tool names to their implementations
- Request Handler: Processes incoming tool calls and returns formatted responses
- Configuration: Manages API keys and model settings
The server runs on stdio (standard input/output) and communicates using JSON-RPC messages
as defined by the MCP protocol.
"""
import asyncio
@@ -9,14 +25,13 @@ import sys
from datetime import datetime
from typing import Any, Dict, List
from google import genai
from mcp.server import Server
from mcp.server.models import InitializationOptions
from mcp.server.stdio import stdio_server
from mcp.types import TextContent, Tool
from config import (
DEFAULT_MODEL,
GEMINI_MODEL,
MAX_CONTEXT_TOKENS,
__author__,
__updated__,
@@ -31,41 +46,67 @@ from tools import (
ThinkDeeperTool,
)
# Configure logging
# Configure logging for server operations
# Set to INFO level to capture important operational messages without being too verbose
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)
# Create the MCP server instance
# Create the MCP server instance with a unique name identifier
# This name is used by MCP clients to identify and connect to this specific server
server: Server = Server("gemini-server")
# Initialize tools
# Initialize the tool registry with all available AI-powered tools
# Each tool provides specialized functionality for different development tasks
# Tools are instantiated once and reused across requests (stateless design)
TOOLS = {
"think_deeper": ThinkDeeperTool(),
"review_code": ReviewCodeTool(),
"debug_issue": DebugIssueTool(),
"analyze": AnalyzeTool(),
"chat": ChatTool(),
"review_changes": ReviewChanges(),
"think_deeper": ThinkDeeperTool(), # Extended reasoning for complex problems
"review_code": ReviewCodeTool(), # Comprehensive code review and quality analysis
"debug_issue": DebugIssueTool(), # Root cause analysis and debugging assistance
"analyze": AnalyzeTool(), # General-purpose file and code analysis
"chat": ChatTool(), # Interactive development chat and brainstorming
"review_changes": ReviewChanges(), # Pre-commit review of git changes
}
def configure_gemini():
"""Configure Gemini API with the provided API key"""
"""
Configure Gemini API with the provided API key.
This function validates that the GEMINI_API_KEY environment variable is set.
The actual API key is used when creating Gemini clients within individual tools
to ensure proper isolation and error handling.
Raises:
ValueError: If GEMINI_API_KEY environment variable is not set
"""
api_key = os.getenv("GEMINI_API_KEY")
if not api_key:
raise ValueError(
"GEMINI_API_KEY environment variable is required. "
"Please set it with your Gemini API key."
)
# API key is used when creating clients in tools
# Note: We don't store the API key globally for security reasons
# Each tool creates its own Gemini client with the API key when needed
logger.info("Gemini API key found")
@server.list_tools()
async def handle_list_tools() -> List[Tool]:
"""List all available tools with verbose descriptions"""
"""
List all available tools with their descriptions and input schemas.
This handler is called by MCP clients during initialization to discover
what tools are available. Each tool provides:
- name: Unique identifier for the tool
- description: Detailed explanation of what the tool does
- inputSchema: JSON Schema defining the expected parameters
Returns:
List of Tool objects representing all available tools
"""
tools = []
# Add all registered AI-powered tools from the TOOLS registry
for tool in TOOLS.values():
tools.append(
Tool(
@@ -75,17 +116,10 @@ async def handle_list_tools() -> List[Tool]:
)
)
# Add utility tools
# Add utility tools that provide server metadata and configuration info
# These tools don't require AI processing but are useful for clients
tools.extend(
[
Tool(
name="list_models",
description=(
"LIST AVAILABLE MODELS - Show all Gemini models you can use. "
"Lists model names, descriptions, and which one is the default."
),
inputSchema={"type": "object", "properties": {}},
),
Tool(
name="get_version",
description=(
@@ -102,100 +136,65 @@ async def handle_list_tools() -> List[Tool]:
@server.call_tool()
async def handle_call_tool(name: str, arguments: Dict[str, Any]) -> List[TextContent]:
"""Handle tool execution requests"""
"""
Handle incoming tool execution requests from MCP clients.
# Handle dynamic tools
This is the main request dispatcher that routes tool calls to their
appropriate handlers. It supports both AI-powered tools (from TOOLS registry)
and utility tools (implemented as static functions).
Args:
name: The name of the tool to execute
arguments: Dictionary of arguments to pass to the tool
Returns:
List of TextContent objects containing the tool's response
"""
# Route to AI-powered tools that require Gemini API calls
if name in TOOLS:
tool = TOOLS[name]
return await tool.execute(arguments)
# Handle static tools
elif name == "list_models":
return await handle_list_models()
# Route to utility tools that provide server information
elif name == "get_version":
return await handle_get_version()
# Handle unknown tool requests gracefully
else:
return [TextContent(type="text", text=f"Unknown tool: {name}")]
async def handle_list_models() -> List[TextContent]:
"""List available Gemini models"""
try:
import json
# Get API key
api_key = os.getenv("GEMINI_API_KEY")
if not api_key:
return [TextContent(type="text", text="Error: GEMINI_API_KEY not set")]
client = genai.Client(api_key=api_key)
models = []
# List models using the new API
try:
model_list = client.models.list()
for model_info in model_list:
models.append(
{
"name": getattr(model_info, "id", "Unknown"),
"display_name": getattr(
model_info,
"display_name",
getattr(model_info, "id", "Unknown"),
),
"description": getattr(
model_info, "description", "No description"
),
"is_default": getattr(model_info, "id", "").endswith(
DEFAULT_MODEL
),
}
)
except Exception:
# Fallback: return some known models
models = [
{
"name": "gemini-2.5-pro-preview-06-05",
"display_name": "Gemini 2.5 Pro",
"description": "Latest Gemini 2.5 Pro model",
"is_default": True,
},
{
"name": "gemini-2.0-flash-thinking-exp",
"display_name": "Gemini 2.0 Flash Thinking",
"description": "Enhanced reasoning model",
"is_default": False,
},
]
return [TextContent(type="text", text=json.dumps(models, indent=2))]
except Exception as e:
return [TextContent(type="text", text=f"Error listing models: {str(e)}")]
async def handle_get_version() -> List[TextContent]:
"""Get version and configuration information"""
"""
Get comprehensive version and configuration information about the server.
Provides details about the server version, configuration settings,
available tools, and runtime environment. Useful for debugging and
understanding the server's capabilities.
Returns:
Formatted text with version and configuration details
"""
# Gather comprehensive server information
version_info = {
"version": __version__,
"updated": __updated__,
"author": __author__,
"default_model": DEFAULT_MODEL,
"gemini_model": GEMINI_MODEL,
"max_context_tokens": f"{MAX_CONTEXT_TOKENS:,}",
"python_version": f"{sys.version_info.major}.{sys.version_info.minor}.{sys.version_info.micro}",
"server_started": datetime.now().isoformat(),
"available_tools": list(TOOLS.keys()) + ["chat", "list_models", "get_version"],
"available_tools": list(TOOLS.keys()) + ["get_version"],
}
# Format the information in a human-readable way
text = f"""Gemini MCP Server v{__version__}
Updated: {__updated__}
Author: {__author__}
Configuration:
- Default Model: {DEFAULT_MODEL}
- Gemini Model: {GEMINI_MODEL}
- Max Context: {MAX_CONTEXT_TOKENS:,} tokens
- Python: {version_info['python_version']}
- Started: {version_info['server_started']}
@@ -209,11 +208,21 @@ For updates, visit: https://github.com/BeehiveInnovations/gemini-mcp-server"""
async def main():
"""Main entry point for the server"""
# Configure Gemini API
"""
Main entry point for the MCP server.
Initializes the Gemini API configuration and starts the server using
stdio transport. The server will continue running until the client
disconnects or an error occurs.
The server communicates via standard input/output streams using the
MCP protocol's JSON-RPC message format.
"""
# Validate that Gemini API key is available before starting
configure_gemini()
# Run the server using stdio transport
# Run the server using stdio transport (standard input/output)
# This allows the server to be launched by MCP clients as a subprocess
async with stdio_server() as (read_stream, write_stream):
await server.run(
read_stream,
@@ -221,7 +230,7 @@ async def main():
InitializationOptions(
server_name="gemini",
server_version=__version__,
capabilities={"tools": {}},
capabilities={"tools": {}}, # Advertise tool support capability
),
)

2
setup.py
View File

@@ -24,7 +24,7 @@ setup(
install_requires=[
"mcp>=1.0.0",
"google-genai>=1.19.0",
"python-dotenv>=1.0.0",
"pydantic>=2.0.0",
],
extras_require={
"dev": [

4
tests/test_config.py
View File

@@ -3,7 +3,7 @@ Tests for configuration
"""
from config import (
DEFAULT_MODEL,
GEMINI_MODEL,
MAX_CONTEXT_TOKENS,
TEMPERATURE_ANALYTICAL,
TEMPERATURE_BALANCED,
@@ -31,7 +31,7 @@ class TestConfig:
def test_model_config(self):
"""Test model configuration"""
assert DEFAULT_MODEL == "gemini-2.5-pro-preview-06-05"
assert GEMINI_MODEL == "gemini-2.5-pro-preview-06-05"
assert MAX_CONTEXT_TOKENS == 1_000_000
def test_temperature_defaults(self):

22
tests/test_server.py
View File

@@ -2,7 +2,6 @@
Tests for the main server functionality
"""
import json
from unittest.mock import Mock, patch
import pytest
@@ -26,11 +25,10 @@ class TestServerTools:
assert "analyze" in tool_names
assert "chat" in tool_names
assert "review_changes" in tool_names
assert "list_models" in tool_names
assert "get_version" in tool_names
# Should have exactly 8 tools
assert len(tools) == 8
# Should have exactly 7 tools
assert len(tools) == 7
# Check descriptions are verbose
for tool in tools:
@@ -69,22 +67,6 @@ class TestServerTools:
assert response_data["status"] == "success"
assert response_data["content"] == "Chat response"
@pytest.mark.asyncio
async def test_handle_list_models(self):
"""Test listing models"""
result = await handle_call_tool("list_models", {})
assert len(result) == 1
# Check if we got models or an error
text = result[0].text
if "Error" in text:
# API key not set in test environment
assert "GEMINI_API_KEY" in text
else:
# Should have models
models = json.loads(text)
assert len(models) >= 1
@pytest.mark.asyncio
async def test_handle_get_version(self):
"""Test getting version info"""

260
tools/base.py
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@@ -1,5 +1,16 @@
"""
Base class for all Gemini MCP tools
This module provides the abstract base class that all tools must inherit from.
It defines the contract that tools must implement and provides common functionality
for request validation, error handling, and response formatting.
Key responsibilities:
- Define the tool interface (abstract methods that must be implemented)
- Handle request validation and file path security
- Manage Gemini model creation with appropriate configurations
- Standardize response formatting and error handling
- Support for clarification requests when more information is needed
"""
from abc import ABC, abstractmethod
@@ -16,7 +27,13 @@ from .models import ToolOutput, ClarificationRequest
class ToolRequest(BaseModel):
"""Base request model for all tools"""
"""
Base request model for all tools.
This Pydantic model defines common parameters that can be used by any tool.
Tools can extend this model to add their specific parameters while inheriting
these common fields.
"""
model: Optional[str] = Field(
None, description="Model to use (defaults to Gemini 2.5 Pro)"
@@ -24,6 +41,8 @@ class ToolRequest(BaseModel):
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(
None,
description="Thinking depth: minimal (128), low (2048), medium (8192), high (16384), max (32768)",
@@ -31,52 +50,130 @@ class ToolRequest(BaseModel):
class BaseTool(ABC):
"""Base class for all Gemini tools"""
"""
Abstract base class for all Gemini tools.
This class defines the interface that all tools must implement and provides
common functionality for request handling, model creation, and response formatting.
To create a new tool:
1. Create a new class that inherits from BaseTool
2. Implement all abstract methods
3. Define a request model that inherits from ToolRequest
4. Register the tool in server.py's TOOLS dictionary
"""
def __init__(self):
# Cache tool metadata at initialization to avoid repeated calls
self.name = self.get_name()
self.description = self.get_description()
self.default_temperature = self.get_default_temperature()
@abstractmethod
def get_name(self) -> str:
"""Return the tool name"""
"""
Return the unique name identifier for this tool.
This name is used by MCP clients to invoke the tool and must be
unique across all registered tools.
Returns:
str: The tool's unique name (e.g., "review_code", "analyze")
"""
pass
@abstractmethod
def get_description(self) -> str:
"""Return the verbose tool description for Claude"""
"""
Return a detailed description of what this tool does.
This description is shown to MCP clients (like Claude) to help them
understand when and how to use the tool. It should be comprehensive
and include trigger phrases.
Returns:
str: Detailed tool description with usage examples
"""
pass
@abstractmethod
def get_input_schema(self) -> Dict[str, Any]:
"""Return the JSON schema for tool inputs"""
"""
Return the JSON Schema that defines this tool's parameters.
This schema is used by MCP clients to validate inputs before
sending requests. It should match the tool's request model.
Returns:
Dict[str, Any]: JSON Schema object defining required and optional parameters
"""
pass
@abstractmethod
def get_system_prompt(self) -> str:
"""Return the system prompt for this tool"""
"""
Return the system prompt that configures the AI model's behavior.
This prompt sets the context and instructions for how the model
should approach the task. It's prepended to the user's request.
Returns:
str: System prompt with role definition and instructions
"""
pass
def get_default_temperature(self) -> float:
"""Return default temperature for this tool"""
"""
Return the default temperature setting for this tool.
Override this method to set tool-specific temperature defaults.
Lower values (0.0-0.3) for analytical tasks, higher (0.7-1.0) for creative tasks.
Returns:
float: Default temperature between 0.0 and 1.0
"""
return 0.5
def get_default_thinking_mode(self) -> str:
"""Return default thinking_mode for this tool"""
"""
Return the default thinking mode for this tool.
Thinking mode controls computational budget for reasoning.
Override for tools that need more or less reasoning depth.
Returns:
str: One of "minimal", "low", "medium", "high", "max"
"""
return "medium" # Default to medium thinking for better reasoning
@abstractmethod
def get_request_model(self):
"""Return the Pydantic model for request validation"""
"""
Return the Pydantic model class used for validating requests.
This model should inherit from ToolRequest and define all
parameters specific to this tool.
Returns:
Type[ToolRequest]: The request model class
"""
pass
def validate_file_paths(self, request) -> Optional[str]:
"""
Validate that all file paths in the request are absolute.
Returns error message if validation fails, None if all paths are valid.
This is a critical security function that prevents path traversal attacks
and ensures all file access is properly controlled. All file paths must
be absolute to avoid ambiguity and security issues.
Args:
request: The validated request object
Returns:
Optional[str]: Error message if validation fails, None if all paths are valid
"""
# Check if request has 'files' attribute
# Check if request has 'files' attribute (used by most tools)
if hasattr(request, "files") and request.files:
for file_path in request.files:
if not os.path.isabs(file_path):
@@ -86,7 +183,7 @@ class BaseTool(ABC):
f"Please provide the full absolute path starting with '/'"
)
# Check if request has 'path' attribute (for review_changes)
# Check if request has 'path' attribute (used by review_changes tool)
if hasattr(request, "path") and request.path:
if not os.path.isabs(request.path):
return (
@@ -98,13 +195,31 @@ class BaseTool(ABC):
return None
async def execute(self, arguments: Dict[str, Any]) -> List[TextContent]:
"""Execute the tool with given arguments"""
"""
Execute the tool with the provided arguments.
This is the main entry point for tool execution. It handles:
1. Request validation using the tool's Pydantic model
2. File path security validation
3. Prompt preparation
4. Model creation and configuration
5. Response generation and formatting
6. Error handling and recovery
Args:
arguments: Dictionary of arguments from the MCP client
Returns:
List[TextContent]: Formatted response as MCP TextContent objects
"""
try:
# Validate request
# Validate request using the tool's Pydantic model
# This ensures all required fields are present and properly typed
request_model = self.get_request_model()
request = request_model(**arguments)
# Validate file paths
# Validate file paths for security
# This prevents path traversal attacks and ensures proper access control
path_error = self.validate_file_paths(request)
if path_error:
error_output = ToolOutput(
@@ -114,13 +229,14 @@ class BaseTool(ABC):
)
return [TextContent(type="text", text=error_output.model_dump_json())]
# Prepare the prompt
# Prepare the full prompt by combining system prompt with user request
# This is delegated to the tool implementation for customization
prompt = await self.prepare_prompt(request)
# Get model configuration
from config import DEFAULT_MODEL
# Extract model configuration from request or use defaults
from config import GEMINI_MODEL
model_name = getattr(request, "model", None) or DEFAULT_MODEL
model_name = getattr(request, "model", None) or GEMINI_MODEL
temperature = getattr(request, "temperature", None)
if temperature is None:
temperature = self.get_default_temperature()
@@ -128,20 +244,23 @@ class BaseTool(ABC):
if thinking_mode is None:
thinking_mode = self.get_default_thinking_mode()
# Create and configure model
# Create model instance with appropriate configuration
# This handles both regular models and thinking-enabled models
model = self.create_model(model_name, temperature, thinking_mode)
# Generate response
# Generate AI response using the configured model
response = model.generate_content(prompt)
# Handle response and create standardized output
# Process the model's response
if response.candidates and response.candidates[0].content.parts:
raw_text = response.candidates[0].content.parts[0].text
# Check if this is a clarification request
# Parse response to check for clarification requests or format output
tool_output = self._parse_response(raw_text, request)
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
@@ -153,10 +272,12 @@ class BaseTool(ABC):
content_type="text",
)
# Serialize the standardized output as JSON
# Return standardized JSON response for consistent client handling
return [TextContent(type="text", text=tool_output.model_dump_json())]
except Exception as e:
# Catch all exceptions to prevent server crashes
# Return error information in standardized format
error_output = ToolOutput(
status="error",
content=f"Error in {self.name}: {str(e)}",
@@ -165,7 +286,19 @@ class BaseTool(ABC):
return [TextContent(type="text", text=error_output.model_dump_json())]
def _parse_response(self, raw_text: str, request) -> ToolOutput:
"""Parse the raw response and determine if it's a clarification request"""
"""
Parse the raw response and determine if it's a clarification request.
Some tools may return JSON indicating they need more information.
This method detects such responses and formats them appropriately.
Args:
raw_text: The raw text response from the model
request: The original request for context
Returns:
ToolOutput: Standardized output object
"""
try:
# Try to parse as JSON to check for clarification requests
potential_json = json.loads(raw_text.strip())
@@ -214,40 +347,79 @@ class BaseTool(ABC):
@abstractmethod
async def prepare_prompt(self, request) -> str:
"""Prepare the full prompt for Gemini"""
"""
Prepare the complete prompt for the Gemini model.
This method should combine the system prompt with the user's request
and any additional context (like file contents) needed for the task.
Args:
request: The validated request object
Returns:
str: Complete prompt ready for the model
"""
pass
def format_response(self, response: str, request) -> str:
"""Format the response for display (can be overridden)"""
"""
Format the model's response for display.
Override this method to add tool-specific formatting like headers,
summaries, or structured output. Default implementation returns
the response unchanged.
Args:
response: The raw response from the model
request: The original request for context
Returns:
str: Formatted response
"""
return response
def create_model(
self, model_name: str, temperature: float, thinking_mode: str = "medium"
):
"""Create a configured Gemini model with thinking configuration"""
# Map thinking modes to budget values
"""
Create a configured Gemini model instance.
This method handles model creation with appropriate settings including
temperature and thinking budget configuration for models that support it.
Args:
model_name: Name of the Gemini model to use
temperature: Temperature setting for response generation
thinking_mode: Thinking depth mode (affects computational budget)
Returns:
Model instance configured and ready for generation
"""
# Map thinking modes to computational budget values
# Higher budgets allow for more complex reasoning but increase latency
thinking_budgets = {
"minimal": 128, # Minimum for 2.5 Pro
"low": 2048,
"medium": 8192,
"high": 16384,
"max": 32768,
"minimal": 128, # Minimum for 2.5 Pro - fast responses
"low": 2048, # Light reasoning tasks
"medium": 8192, # Balanced reasoning (default)
"high": 16384, # Complex analysis
"max": 32768, # Maximum reasoning depth
}
thinking_budget = thinking_budgets.get(thinking_mode, 8192)
# For models supporting thinking config, use the new API
# Skip in test environment to allow mocking
# Gemini 2.5 models support thinking configuration for enhanced reasoning
# Skip special handling in test environment to allow mocking
if "2.5" in model_name and not os.environ.get("PYTEST_CURRENT_TEST"):
try:
# Get API key
# Retrieve API key for Gemini client creation
api_key = os.environ.get("GEMINI_API_KEY")
if not api_key:
raise ValueError("GEMINI_API_KEY environment variable is required")
client = genai.Client(api_key=api_key)
# Create a wrapper to match the expected interface
# 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
@@ -270,7 +442,8 @@ class BaseTool(ABC):
),
)
# Convert to match expected format
# Wrap the response to match the expected format
# This ensures compatibility across different API versions
class ResponseWrapper:
def __init__(self, text):
self.text = text
@@ -302,18 +475,19 @@ class BaseTool(ABC):
return ModelWrapper(client, model_name, temperature, thinking_budget)
except Exception:
# Fall back to regular genai model if new API fails
# Fall back to regular API if thinking configuration fails
# This ensures the tool remains functional even with API changes
pass
# For non-2.5 models or if thinking not needed, use regular API
# Get API key
# For models that don't support thinking configuration, use standard API
api_key = os.environ.get("GEMINI_API_KEY")
if not api_key:
raise ValueError("GEMINI_API_KEY environment variable is required")
client = genai.Client(api_key=api_key)
# Create wrapper for consistency
# Create a simple wrapper for models without thinking configuration
# This provides the same interface as the thinking-enabled wrapper
class SimpleModelWrapper:
def __init__(self, client, model_name, temperature):
self.client = client

69
tools/review_code.py
View File

@@ -1,5 +1,17 @@
"""
Code Review tool - Comprehensive code analysis and review
This tool provides professional-grade code review capabilities using
Gemini'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, Dict, List, Optional
@@ -14,7 +26,13 @@ from .base import BaseTool, ToolRequest
class ReviewCodeRequest(ToolRequest):
"""Request model for review_code tool"""
"""
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(
...,
@@ -36,7 +54,13 @@ class ReviewCodeRequest(ToolRequest):
class ReviewCodeTool(BaseTool):
"""Professional code review tool"""
"""
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 "review_code"
@@ -105,11 +129,25 @@ class ReviewCodeTool(BaseTool):
return ReviewCodeRequest
async def prepare_prompt(self, request: ReviewCodeRequest) -> str:
"""Prepare the code review prompt"""
# Read all files
"""
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 Gemini model
Raises:
ValueError: If the code exceeds token limits
"""
# Read all requested files, expanding directories as needed
file_content, summary = read_files(request.files)
# Check token limits
# Validate that the code fits within model context limits
within_limit, estimated_tokens = check_token_limit(file_content)
if not within_limit:
raise ValueError(
@@ -117,7 +155,7 @@ class ReviewCodeTool(BaseTool):
f"Maximum is {MAX_CONTEXT_TOKENS:,} tokens."
)
# Build review instructions
# Build customized review instructions based on review type
review_focus = []
if request.review_type == "security":
review_focus.append(
@@ -132,12 +170,15 @@ class ReviewCodeTool(BaseTool):
"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"
@@ -145,7 +186,7 @@ class ReviewCodeTool(BaseTool):
focus_instruction = "\n".join(review_focus) if review_focus else ""
# Combine everything
# Construct the complete prompt with system instructions and code
full_prompt = f"""{self.get_system_prompt()}
{focus_instruction}
@@ -159,7 +200,19 @@ Please provide a comprehensive code review following the format specified in the
return full_prompt
def format_response(self, response: str, request: ReviewCodeRequest) -> str:
"""Format the review response"""
"""
Format the review response with appropriate headers.
Adds context about the review type and focus area to help
users understand the scope of the review.
Args:
response: The raw review from the model
request: The original request for context
Returns:
str: Formatted response with headers
"""
header = f"Code Review ({request.review_type.upper()})"
if request.focus_on:
header += f" - Focus: {request.focus_on}"

130
utils/file_utils.py
View File

@@ -1,5 +1,21 @@
"""
File reading utilities with directory support and token management
This module provides secure file access functionality for the MCP server.
It implements critical security measures to prevent unauthorized file access
and manages token limits to ensure efficient API usage.
Key Features:
- Path validation and sandboxing to prevent directory traversal attacks
- Support for both individual files and recursive directory reading
- Token counting and management to stay within API limits
- Automatic file type detection and filtering
- Comprehensive error handling with informative messages
Security Model:
- All file access is restricted to PROJECT_ROOT and its subdirectories
- Absolute paths are required to prevent ambiguity
- Symbolic links are resolved to ensure they stay within bounds
"""
import os
@@ -10,9 +26,12 @@ from .token_utils import estimate_tokens, MAX_CONTEXT_TOKENS
# Get project root from environment or use current directory
# This defines the sandbox directory where file access is allowed
# Security: All file operations are restricted to this directory and its children
PROJECT_ROOT = Path(os.environ.get("MCP_PROJECT_ROOT", os.getcwd())).resolve()
# Security: Prevent running with overly permissive root
# Critical Security Check: Prevent running with overly permissive root
# Setting PROJECT_ROOT to "/" would allow access to the entire filesystem,
# which is a severe security vulnerability
if str(PROJECT_ROOT) == "/":
raise RuntimeError(
"Security Error: MCP_PROJECT_ROOT cannot be set to '/'. "
@@ -20,7 +39,8 @@ if str(PROJECT_ROOT) == "/":
)
# Common code file extensions
# Common code file extensions that are automatically included when processing directories
# This set can be extended to support additional file types
CODE_EXTENSIONS = {
".py",
".js",
@@ -75,11 +95,16 @@ def resolve_and_validate_path(path_str: str) -> Path:
"""
Validates that a path is absolute and resolves it.
This is the primary security function that ensures all file access
is properly sandboxed. It enforces two critical security policies:
1. All paths must be absolute (no ambiguity)
2. All paths must resolve to within PROJECT_ROOT (sandboxing)
Args:
path_str: Path string (must be absolute)
Returns:
Resolved Path object
Resolved Path object that is guaranteed to be within PROJECT_ROOT
Raises:
ValueError: If path is not absolute
@@ -88,17 +113,19 @@ def resolve_and_validate_path(path_str: str) -> Path:
# Create a Path object from the user-provided path
user_path = Path(path_str)
# Require absolute paths
# Security Policy 1: Require absolute paths to prevent ambiguity
# Relative paths could be interpreted differently depending on working directory
if not user_path.is_absolute():
raise ValueError(
f"Relative paths are not supported. Please provide an absolute path.\n"
f"Received: {path_str}"
)
# Resolve the absolute path
# Resolve the absolute path (follows symlinks, removes .. and .)
resolved_path = user_path.resolve()
# Security check: ensure the resolved path is within PROJECT_ROOT
# Security Policy 2: Ensure the resolved path is within PROJECT_ROOT
# This prevents directory traversal attacks (e.g., /project/../../../etc/passwd)
try:
resolved_path.relative_to(PROJECT_ROOT)
except ValueError:
@@ -115,12 +142,16 @@ def expand_paths(paths: List[str], extensions: Optional[Set[str]] = None) -> Lis
"""
Expand paths to individual files, handling both files and directories.
This function recursively walks directories to find all matching files.
It automatically filters out hidden files and common non-code directories
like __pycache__ to avoid including generated or system files.
Args:
paths: List of file or directory paths
extensions: Optional set of file extensions to include
paths: List of file or directory paths (must be absolute)
extensions: Optional set of file extensions to include (defaults to CODE_EXTENSIONS)
Returns:
List of individual file paths
List of individual file paths, sorted for consistent ordering
"""
if extensions is None:
extensions = CODE_EXTENSIONS
@@ -130,9 +161,10 @@ def expand_paths(paths: List[str], extensions: Optional[Set[str]] = None) -> Lis
for path in paths:
try:
# Validate each path for security before processing
path_obj = resolve_and_validate_path(path)
except (ValueError, PermissionError):
# Skip invalid paths
# Skip invalid paths silently to allow partial success
continue
if not path_obj.exists():
@@ -145,51 +177,61 @@ def expand_paths(paths: List[str], extensions: Optional[Set[str]] = None) -> Lis
seen.add(str(path_obj))
elif path_obj.is_dir():
# Walk directory recursively
# Walk directory recursively to find all files
for root, dirs, files in os.walk(path_obj):
# Skip hidden directories and __pycache__
# Filter directories in-place to skip hidden and cache directories
# This prevents descending into .git, .venv, __pycache__, etc.
dirs[:] = [
d for d in dirs if not d.startswith(".") and d != "__pycache__"
]
for file in files:
# Skip hidden files
# Skip hidden files (e.g., .DS_Store, .gitignore)
if file.startswith("."):
continue
file_path = Path(root) / file
# Check extension
# Filter by extension if specified
if not extensions or file_path.suffix.lower() in extensions:
full_path = str(file_path)
# Use set to prevent duplicates
if full_path not in seen:
expanded_files.append(full_path)
seen.add(full_path)
# Sort for consistent ordering
# Sort for consistent ordering across different runs
# This makes output predictable and easier to debug
expanded_files.sort()
return expanded_files
def read_file_content(file_path: str, max_size: int = 1_000_000) -> Tuple[str, int]:
"""
Read a single file and format it for Gemini.
Read a single file and format it for inclusion in AI prompts.
This function handles various error conditions gracefully and always
returns formatted content, even for errors. This ensures the AI model
gets context about what files were attempted but couldn't be read.
Args:
file_path: Path to file (must be absolute)
max_size: Maximum file size to read
max_size: Maximum file size to read (default 1MB to prevent memory issues)
Returns:
(formatted_content, estimated_tokens)
Tuple of (formatted_content, estimated_tokens)
Content is wrapped with clear delimiters for AI parsing
"""
try:
# Validate path security before any file operations
path = resolve_and_validate_path(file_path)
except (ValueError, PermissionError) as e:
# Return error in a format that provides context to the AI
content = f"\n--- ERROR ACCESSING FILE: {file_path} ---\nError: {str(e)}\n--- END FILE ---\n"
return content, estimate_tokens(content)
try:
# Check if path exists and is a file
# Validate file existence and type
if not path.exists():
content = f"\n--- FILE NOT FOUND: {file_path} ---\nError: File does not exist\n--- END FILE ---\n"
return content, estimate_tokens(content)
@@ -198,17 +240,19 @@ def read_file_content(file_path: str, max_size: int = 1_000_000) -> Tuple[str, i
content = f"\n--- NOT A FILE: {file_path} ---\nError: Path is not a file\n--- END FILE ---\n"
return content, estimate_tokens(content)
# Check file size
# Check file size to prevent memory exhaustion
file_size = path.stat().st_size
if file_size > max_size:
content = f"\n--- FILE TOO LARGE: {file_path} ---\nFile size: {file_size:,} bytes (max: {max_size:,})\n--- END FILE ---\n"
return content, estimate_tokens(content)
# Read the file
# Read the file with UTF-8 encoding, replacing invalid characters
# This ensures we can handle files with mixed encodings
with open(path, "r", encoding="utf-8", errors="replace") as f:
file_content = f.read()
# Format with clear delimiters for Gemini
# Format with clear delimiters that help the AI understand file boundaries
# Using consistent markers makes it easier for the model to parse
formatted = f"\n--- BEGIN FILE: {file_path} ---\n{file_content}\n--- END FILE: {file_path} ---\n"
return formatted, estimate_tokens(formatted)
@@ -226,14 +270,21 @@ def read_files(
"""
Read multiple files and optional direct code with smart token management.
This function implements intelligent token budgeting to maximize the amount
of relevant content that can be included in an AI prompt while staying
within token limits. It prioritizes direct code and reads files until
the token budget is exhausted.
Args:
file_paths: List of file or directory paths
code: Optional direct code to include
file_paths: List of file or directory paths (absolute paths required)
code: Optional direct code to include (prioritized over files)
max_tokens: Maximum tokens to use (defaults to MAX_CONTEXT_TOKENS)
reserve_tokens: Tokens to reserve for prompt and response
reserve_tokens: Tokens to reserve for prompt and response (default 50K)
Returns:
(full_content, brief_summary)
Tuple of (full_content, brief_summary)
- full_content: All file contents formatted for AI consumption
- brief_summary: Human-readable summary of what was processed
"""
if max_tokens is None:
max_tokens = MAX_CONTEXT_TOKENS
@@ -247,7 +298,8 @@ def read_files(
files_skipped = []
dirs_processed = []
# First, handle direct code if provided
# Priority 1: Handle direct code if provided
# Direct code is prioritized because it's explicitly provided by the user
if code:
formatted_code = (
f"\n--- BEGIN DIRECT CODE ---\n{code}\n--- END DIRECT CODE ---\n"
@@ -258,19 +310,23 @@ def read_files(
content_parts.append(formatted_code)
total_tokens += code_tokens
available_tokens -= code_tokens
# Create a preview for the summary
code_preview = code[:50] + "..." if len(code) > 50 else code
summary_parts.append(f"Direct code: {code_preview}")
else:
summary_parts.append("Direct code skipped (too large)")
# Expand all paths to get individual files
# Priority 2: Process file paths
if file_paths:
# Track which paths are directories
# Track which paths are directories for summary
for path in file_paths:
if Path(path).is_dir():
dirs_processed.append(path)
try:
if Path(path).is_dir():
dirs_processed.append(path)
except Exception:
pass # Ignore invalid paths
# Expand to get all files
# Expand directories to get all individual files
all_files = expand_paths(file_paths)
if not all_files and file_paths:
@@ -279,7 +335,7 @@ def read_files(
f"\n--- NO FILES FOUND ---\nProvided paths: {', '.join(file_paths)}\n--- END ---\n"
)
else:
# Read files up to token limit
# Read files sequentially until token limit is reached
for file_path in all_files:
if total_tokens >= available_tokens:
files_skipped.append(file_path)
@@ -293,9 +349,10 @@ def read_files(
total_tokens += file_tokens
files_read.append(file_path)
else:
# File too large for remaining budget
files_skipped.append(file_path)
# Build summary
# Build human-readable summary of what was processed
if dirs_processed:
summary_parts.append(f"Processed {len(dirs_processed)} dir(s)")
if files_read:
@@ -305,11 +362,12 @@ def read_files(
if total_tokens > 0:
summary_parts.append(f"~{total_tokens:,} tokens used")
# Add skipped files note if any were skipped
# Add informative note about skipped files to help users understand
# what was omitted and why
if files_skipped:
skip_note = "\n\n--- SKIPPED FILES (TOKEN LIMIT) ---\n"
skip_note += f"Total skipped: {len(files_skipped)}\n"
# Show first 10 skipped files
# Show first 10 skipped files as examples
for i, file_path in enumerate(files_skipped[:10]):
skip_note += f" - {file_path}\n"
if len(files_skipped) > 10:

122
utils/git_utils.py
View File

@@ -1,5 +1,20 @@
"""
Git utilities for finding repositories and generating diffs.
This module provides Git integration functionality for the MCP server,
enabling tools to work with version control information. It handles
repository discovery, status checking, and diff generation.
Key Features:
- Recursive repository discovery with depth limits
- Safe command execution with timeouts
- Comprehensive status information extraction
- Support for staged and unstaged changes
Security Considerations:
- All git commands are run with timeouts to prevent hanging
- Repository discovery ignores common build/dependency directories
- Error handling for permission-denied scenarios
"""
import subprocess
@@ -8,16 +23,18 @@ from pathlib import Path
# Directories to ignore when searching for git repositories
# These are typically build artifacts, dependencies, or cache directories
# that don't contain source code and would slow down repository discovery
IGNORED_DIRS = {
"node_modules",
"__pycache__",
"venv",
"env",
"build",
"dist",
"target",
".tox",
".pytest_cache",
"node_modules", # Node.js dependencies
"__pycache__", # Python bytecode cache
"venv", # Python virtual environment
"env", # Alternative virtual environment name
"build", # Common build output directory
"dist", # Distribution/release builds
"target", # Maven/Rust build output
".tox", # Tox testing environments
".pytest_cache", # Pytest cache directory
}
@@ -25,38 +42,45 @@ def find_git_repositories(start_path: str, max_depth: int = 5) -> List[str]:
"""
Recursively find all git repositories starting from the given path.
This function walks the directory tree looking for .git directories,
which indicate the root of a git repository. It respects depth limits
to prevent excessive recursion in deep directory structures.
Args:
start_path: Directory to start searching from
max_depth: Maximum depth to search (prevents excessive recursion)
start_path: Directory to start searching from (must be absolute)
max_depth: Maximum depth to search (default 5 prevents excessive recursion)
Returns:
List of absolute paths to git repositories
List of absolute paths to git repositories, sorted alphabetically
"""
repositories = []
start_path = Path(start_path).resolve()
def _find_repos(current_path: Path, current_depth: int):
# Stop recursion if we've reached maximum depth
if current_depth > max_depth:
return
try:
# Check if current directory is a git repo
# Check if current directory contains a .git directory
git_dir = current_path / ".git"
if git_dir.exists() and git_dir.is_dir():
repositories.append(str(current_path))
# Don't search inside .git directory
# Don't search inside git repositories for nested repos
# This prevents finding submodules which should be handled separately
return
# Search subdirectories
# Search subdirectories for more repositories
for item in current_path.iterdir():
if item.is_dir() and not item.name.startswith("."):
# Skip common non-code directories
# Skip common non-code directories to improve performance
if item.name in IGNORED_DIRS:
continue
_find_repos(item, current_depth + 1)
except PermissionError:
# Skip directories we can't access
# Skip directories we don't have permission to read
# This is common for system directories or other users' files
pass
_find_repos(start_path, 0)
@@ -67,16 +91,28 @@ def run_git_command(repo_path: str, command: List[str]) -> Tuple[bool, str]:
"""
Run a git command in the specified repository.
This function provides a safe way to execute git commands with:
- Timeout protection (30 seconds) to prevent hanging
- Proper error handling and output capture
- Working directory context management
Args:
repo_path: Path to the git repository
command: Git command as a list of arguments
repo_path: Path to the git repository (working directory)
command: Git command as a list of arguments (excluding 'git' itself)
Returns:
Tuple of (success, output/error)
- success: True if command returned 0, False otherwise
- output/error: stdout if successful, stderr or error message if failed
"""
try:
# Execute git command with safety measures
result = subprocess.run(
["git"] + command, cwd=repo_path, capture_output=True, text=True, timeout=30
["git"] + command,
cwd=repo_path, # Run in repository directory
capture_output=True, # Capture stdout and stderr
text=True, # Return strings instead of bytes
timeout=30, # Prevent hanging on slow operations
)
if result.returncode == 0:
@@ -85,21 +121,36 @@ def run_git_command(repo_path: str, command: List[str]) -> Tuple[bool, str]:
return False, result.stderr
except subprocess.TimeoutExpired:
return False, "Command timed out"
return False, "Command timed out after 30 seconds"
except Exception as e:
return False, str(e)
return False, f"Git command failed: {str(e)}"
def get_git_status(repo_path: str) -> Dict[str, any]:
"""
Get the current git status of a repository.
Get comprehensive git status information for a repository.
This function gathers various pieces of repository state including:
- Current branch name
- Commits ahead/behind upstream
- Lists of staged, unstaged, and untracked files
The function is resilient to repositories without remotes or
in detached HEAD state.
Args:
repo_path: Path to the git repository
Returns:
Dictionary with status information
Dictionary with status information:
- branch: Current branch name (empty if detached)
- ahead: Number of commits ahead of upstream
- behind: Number of commits behind upstream
- staged_files: List of files with staged changes
- unstaged_files: List of files with unstaged changes
- untracked_files: List of untracked files
"""
# Initialize status structure with default values
status = {
"branch": "",
"ahead": 0,
@@ -109,12 +160,12 @@ def get_git_status(repo_path: str) -> Dict[str, any]:
"untracked_files": [],
}
# Get current branch
# Get current branch name (empty if in detached HEAD state)
success, branch = run_git_command(repo_path, ["branch", "--show-current"])
if success:
status["branch"] = branch.strip()
# Get ahead/behind info
# Get ahead/behind information relative to upstream branch
if status["branch"]:
success, ahead_behind = run_git_command(
repo_path,
@@ -131,33 +182,38 @@ def get_git_status(repo_path: str) -> Dict[str, any]:
if len(parts) == 2:
status["behind"] = int(parts[0])
status["ahead"] = int(parts[1])
# else: Could not get ahead/behind status (branch may not have upstream)
# Note: This will fail gracefully if branch has no upstream set
# Get file status
# Get file status using porcelain format for machine parsing
# Format: XY filename where X=staged status, Y=unstaged status
success, status_output = run_git_command(repo_path, ["status", "--porcelain"])
if success:
for line in status_output.strip().split("\n"):
if not line:
continue
status_code = line[:2]
path_info = line[3:]
status_code = line[:2] # Two-character status code
path_info = line[3:] # Filename (after space)
# Handle staged changes
# Parse staged changes (first character of status code)
if status_code[0] == "R":
# Format is "old_path -> new_path" for renamed files
# Special handling for renamed files
# Format is "old_path -> new_path"
if " -> " in path_info:
_, new_path = path_info.split(" -> ", 1)
status["staged_files"].append(new_path)
else:
status["staged_files"].append(path_info)
elif status_code[0] in ["M", "A", "D", "C"]:
# M=modified, A=added, D=deleted, C=copied
status["staged_files"].append(path_info)
# Handle unstaged changes
# Parse unstaged changes (second character of status code)
if status_code[1] in ["M", "D"]:
# M=modified, D=deleted in working tree
status["unstaged_files"].append(path_info)
elif status_code == "??":
# Untracked files have special marker "??"
status["untracked_files"].append(path_info)
return status

41
utils/token_utils.py
View File

@@ -1,5 +1,12 @@
"""
Token counting utilities
Token counting utilities for managing API context limits
This module provides functions for estimating token counts to ensure
requests stay within the Gemini API's context window limits.
Note: The estimation uses a simple character-to-token ratio which is
approximate. For production systems requiring precise token counts,
consider using the actual tokenizer for the specific model.
"""
from typing import Tuple
@@ -8,14 +15,40 @@ from config import MAX_CONTEXT_TOKENS
def estimate_tokens(text: str) -> int:
"""Estimate token count (rough: 1 token ≈ 4 characters)"""
"""
Estimate token count using a character-based approximation.
This uses a rough heuristic where 1 token ≈ 4 characters, which is
a reasonable approximation for English text. The actual token count
may vary based on:
- Language (non-English text may have different ratios)
- Code vs prose (code often has more tokens per character)
- Special characters and formatting
Args:
text: The text to estimate tokens for
Returns:
int: Estimated number of tokens
"""
return len(text) // 4
def check_token_limit(text: str) -> Tuple[bool, int]:
"""
Check if text exceeds token limit.
Returns: (is_within_limit, estimated_tokens)
Check if text exceeds the maximum token limit for Gemini models.
This function is used to validate that prepared prompts will fit
within the model's context window, preventing API errors and ensuring
reliable operation.
Args:
text: The text to check
Returns:
Tuple[bool, int]: (is_within_limit, estimated_tokens)
- is_within_limit: True if the text fits within MAX_CONTEXT_TOKENS
- estimated_tokens: The estimated token count
"""
estimated = estimate_tokens(text)
return estimated <= MAX_CONTEXT_TOKENS, estimated