mirror of
https://github.com/SuperClaude-Org/SuperClaude_Framework.git
synced 2025-12-17 17:56:46 +00:00
646 lines
27 KiB
Python
646 lines
27 KiB
Python
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#!/usr/bin/env python3
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"""
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SuperClaude-Lite Pre-Tool-Use Hook
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Implements ORCHESTRATOR.md + MCP routing intelligence for optimal tool selection.
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Performance target: <200ms execution time.
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This hook runs before every tool usage and provides:
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- Intelligent tool routing and MCP server selection
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- Performance optimization and parallel execution planning
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- Context-aware tool configuration
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- Fallback strategy implementation
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- Real-time adaptation based on effectiveness
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"""
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import sys
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import json
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import time
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import os
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from pathlib import Path
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from typing import Dict, Any, List, Optional
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# Add shared modules to path
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sys.path.insert(0, os.path.join(os.path.dirname(__file__), "shared"))
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from framework_logic import FrameworkLogic, OperationContext, OperationType, RiskLevel
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from pattern_detection import PatternDetector, PatternMatch
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from mcp_intelligence import MCPIntelligence, MCPActivationPlan
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from compression_engine import CompressionEngine
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from learning_engine import LearningEngine, LearningType, AdaptationScope
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from yaml_loader import config_loader
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from logger import log_hook_start, log_hook_end, log_decision, log_error
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class PreToolUseHook:
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"""
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Pre-tool-use hook implementing SuperClaude orchestration intelligence.
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Responsibilities:
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- Analyze tool usage context and requirements
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- Route to optimal MCP servers based on capability matching
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- Configure parallel execution and performance optimization
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- Apply learned adaptations for tool selection
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- Implement fallback strategies for server failures
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- Track tool effectiveness and performance metrics
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"""
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def __init__(self):
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start_time = time.time()
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# Initialize core components
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self.framework_logic = FrameworkLogic()
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self.pattern_detector = PatternDetector()
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self.mcp_intelligence = MCPIntelligence()
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self.compression_engine = CompressionEngine()
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# Initialize learning engine
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cache_dir = Path("cache")
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self.learning_engine = LearningEngine(cache_dir)
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# Load hook-specific configuration from SuperClaude config
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self.hook_config = config_loader.get_hook_config('pre_tool_use')
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# Load orchestrator configuration (from YAML if exists, otherwise use hook config)
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try:
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self.orchestrator_config = config_loader.load_config('orchestrator')
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except FileNotFoundError:
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# Fall back to hook configuration if YAML file not found
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self.orchestrator_config = self.hook_config.get('configuration', {})
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# Load performance configuration (from YAML if exists, otherwise use hook config)
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try:
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self.performance_config = config_loader.load_config('performance')
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except FileNotFoundError:
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# Fall back to performance targets from global configuration
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self.performance_config = config_loader.get_performance_targets()
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# Performance tracking using configuration
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self.initialization_time = (time.time() - start_time) * 1000
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self.performance_target_ms = config_loader.get_hook_config('pre_tool_use', 'performance_target_ms', 200)
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def process_tool_use(self, tool_request: dict) -> dict:
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"""
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Process tool use request with intelligent routing.
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Args:
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tool_request: Tool usage request from Claude Code
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Returns:
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Enhanced tool configuration with SuperClaude intelligence
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"""
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start_time = time.time()
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# Log hook start
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log_hook_start("pre_tool_use", {
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"tool_name": tool_request.get('tool_name', 'unknown'),
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"has_parameters": bool(tool_request.get('parameters'))
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})
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try:
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# Extract tool context
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context = self._extract_tool_context(tool_request)
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# Analyze tool requirements and capabilities
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requirements = self._analyze_tool_requirements(context)
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# Log routing decision
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if requirements.get('mcp_server_hints'):
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log_decision(
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"pre_tool_use",
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"mcp_server_selection",
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",".join(requirements['mcp_server_hints']),
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f"Tool '{context['tool_name']}' requires capabilities: {', '.join(requirements.get('capabilities_needed', []))}"
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)
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# Detect patterns for intelligent routing
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routing_analysis = self._analyze_routing_patterns(context, requirements)
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# Apply learned adaptations
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enhanced_routing = self._apply_routing_adaptations(context, routing_analysis)
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# Create optimal execution plan
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execution_plan = self._create_execution_plan(context, enhanced_routing)
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# Log execution strategy decision
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log_decision(
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"pre_tool_use",
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"execution_strategy",
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execution_plan['execution_strategy'],
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f"Complexity: {context.get('complexity_score', 0):.2f}, Files: {context.get('file_count', 1)}"
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)
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# Configure tool enhancement
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tool_config = self._configure_tool_enhancement(context, execution_plan)
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# Record learning event
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self._record_tool_learning(context, tool_config)
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# Performance validation
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execution_time = (time.time() - start_time) * 1000
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tool_config['performance_metrics'] = {
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'routing_time_ms': execution_time,
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'target_met': execution_time < self.performance_target_ms,
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'efficiency_score': self._calculate_efficiency_score(context, execution_time)
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}
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# Log successful completion
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log_hook_end(
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"pre_tool_use",
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int(execution_time),
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True,
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{
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"tool_name": context['tool_name'],
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"mcp_servers": tool_config.get('mcp_integration', {}).get('servers', []),
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"enhanced_mode": tool_config.get('enhanced_mode', False)
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}
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)
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return tool_config
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except Exception as e:
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# Log error
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execution_time = (time.time() - start_time) * 1000
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log_error(
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"pre_tool_use",
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str(e),
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{"tool_name": tool_request.get('tool_name', 'unknown')}
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)
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log_hook_end("pre_tool_use", int(execution_time), False)
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# Graceful fallback on error
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return self._create_fallback_tool_config(tool_request, str(e))
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def _extract_tool_context(self, tool_request: dict) -> dict:
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"""Extract and enrich tool usage context."""
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context = {
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'tool_name': tool_request.get('tool_name', ''),
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'tool_parameters': tool_request.get('parameters', {}),
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'user_intent': tool_request.get('user_intent', ''),
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'session_context': tool_request.get('session_context', {}),
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'previous_tools': tool_request.get('previous_tools', []),
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'operation_sequence': tool_request.get('operation_sequence', []),
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'resource_state': tool_request.get('resource_state', {}),
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'timestamp': time.time()
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}
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# Extract operation characteristics
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context.update(self._analyze_operation_characteristics(context))
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# Analyze tool chain context
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context.update(self._analyze_tool_chain_context(context))
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return context
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def _analyze_operation_characteristics(self, context: dict) -> dict:
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"""Analyze operation characteristics for routing decisions."""
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characteristics = {
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'operation_type': OperationType.READ,
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'complexity_score': 0.0,
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'file_count': 1,
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'directory_count': 1,
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'parallelizable': False,
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'resource_intensive': False,
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'requires_intelligence': False
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}
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tool_name = context['tool_name']
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tool_params = context['tool_parameters']
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# Determine operation type from tool
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if tool_name in ['Write', 'Edit', 'MultiEdit']:
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characteristics['operation_type'] = OperationType.WRITE
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characteristics['complexity_score'] += 0.2
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elif tool_name in ['Build', 'Implement']:
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characteristics['operation_type'] = OperationType.BUILD
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characteristics['complexity_score'] += 0.4
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elif tool_name in ['Test', 'Validate']:
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characteristics['operation_type'] = OperationType.TEST
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characteristics['complexity_score'] += 0.1
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elif tool_name in ['Analyze', 'Debug']:
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characteristics['operation_type'] = OperationType.ANALYZE
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characteristics['complexity_score'] += 0.3
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characteristics['requires_intelligence'] = True
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# Analyze file/directory scope
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if 'file_path' in tool_params:
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characteristics['file_count'] = 1
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elif 'files' in tool_params:
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file_list = tool_params['files']
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characteristics['file_count'] = len(file_list) if isinstance(file_list, list) else 1
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if characteristics['file_count'] > 3:
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characteristics['parallelizable'] = True
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characteristics['complexity_score'] += 0.3
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if 'directory' in tool_params or 'path' in tool_params:
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path_param = tool_params.get('directory') or tool_params.get('path', '')
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if '*' in str(path_param) or '**' in str(path_param):
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characteristics['directory_count'] = 5 # Estimate for glob patterns
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characteristics['complexity_score'] += 0.2
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characteristics['parallelizable'] = True
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# Resource intensity analysis
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if characteristics['file_count'] > 10 or characteristics['complexity_score'] > 0.6:
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characteristics['resource_intensive'] = True
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# Intelligence requirements
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intelligence_tools = ['Analyze', 'Debug', 'Optimize', 'Refactor', 'Generate']
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if any(tool in tool_name for tool in intelligence_tools):
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characteristics['requires_intelligence'] = True
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return characteristics
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def _analyze_tool_chain_context(self, context: dict) -> dict:
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"""Analyze tool chain context for optimization opportunities."""
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chain_analysis = {
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'chain_length': len(context['previous_tools']),
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'pattern_detected': None,
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'optimization_opportunity': False,
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'cache_opportunity': False
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}
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previous_tools = context['previous_tools']
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if len(previous_tools) >= 2:
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# Detect common patterns
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tool_names = [tool.get('name', '') for tool in previous_tools[-3:]]
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# Read-Edit pattern
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if any('Read' in name for name in tool_names) and any('Edit' in name for name in tool_names):
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chain_analysis['pattern_detected'] = 'read_edit_pattern'
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chain_analysis['optimization_opportunity'] = True
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# Multiple file operations
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if sum(1 for name in tool_names if 'file' in name.lower()) >= 2:
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chain_analysis['pattern_detected'] = 'multi_file_pattern'
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chain_analysis['optimization_opportunity'] = True
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# Analysis chain
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if sum(1 for name in tool_names if any(word in name for word in ['Analyze', 'Search', 'Find'])) >= 2:
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chain_analysis['pattern_detected'] = 'analysis_chain'
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chain_analysis['cache_opportunity'] = True
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return chain_analysis
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def _analyze_tool_requirements(self, context: dict) -> dict:
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"""Analyze tool requirements for capability matching."""
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requirements = {
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'capabilities_needed': [],
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'performance_requirements': {},
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'quality_requirements': {},
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'mcp_server_hints': [],
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'native_tool_sufficient': True
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}
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tool_name = context['tool_name']
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characteristics = context
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# Determine required capabilities
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if characteristics.get('requires_intelligence'):
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requirements['capabilities_needed'].extend(['analysis', 'reasoning', 'context_understanding'])
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requirements['native_tool_sufficient'] = False
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if characteristics.get('complexity_score', 0) > 0.6:
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requirements['capabilities_needed'].extend(['complex_reasoning', 'systematic_analysis'])
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requirements['mcp_server_hints'].append('sequential')
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if characteristics.get('file_count', 1) > 5:
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requirements['capabilities_needed'].extend(['multi_file_coordination', 'semantic_understanding'])
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requirements['mcp_server_hints'].append('serena')
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# UI/component operations
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if any(word in context.get('user_intent', '').lower() for word in ['component', 'ui', 'frontend', 'design']):
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requirements['capabilities_needed'].append('ui_generation')
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requirements['mcp_server_hints'].append('magic')
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# Documentation/library operations
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if any(word in context.get('user_intent', '').lower() for word in ['library', 'documentation', 'framework', 'api']):
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requirements['capabilities_needed'].append('documentation_access')
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requirements['mcp_server_hints'].append('context7')
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# Testing operations
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if tool_name in ['Test'] or 'test' in context.get('user_intent', '').lower():
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requirements['capabilities_needed'].append('testing_automation')
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requirements['mcp_server_hints'].append('playwright')
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# Performance requirements
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if characteristics.get('resource_intensive'):
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requirements['performance_requirements'] = {
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'max_execution_time_ms': 5000,
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'memory_efficiency_required': True,
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'parallel_execution_preferred': True
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}
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else:
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requirements['performance_requirements'] = {
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'max_execution_time_ms': 2000,
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'response_time_critical': True
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}
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# Quality requirements
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if context.get('session_context', {}).get('is_production', False):
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requirements['quality_requirements'] = {
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'validation_required': True,
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'error_handling_critical': True,
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'rollback_capability_needed': True
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}
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return requirements
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def _analyze_routing_patterns(self, context: dict, requirements: dict) -> dict:
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"""Analyze patterns for intelligent routing decisions."""
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# Create operation data for pattern detection
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operation_data = {
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'operation_type': context.get('operation_type', OperationType.READ).value,
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'file_count': context.get('file_count', 1),
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'complexity_score': context.get('complexity_score', 0.0),
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'tool_name': context['tool_name']
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}
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# Run pattern detection
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detection_result = self.pattern_detector.detect_patterns(
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context.get('user_intent', ''),
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context,
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operation_data
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)
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# Create MCP activation plan
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||
|
|
mcp_plan = self.mcp_intelligence.create_activation_plan(
|
||
|
|
context.get('user_intent', ''),
|
||
|
|
context,
|
||
|
|
operation_data
|
||
|
|
)
|
||
|
|
|
||
|
|
return {
|
||
|
|
'pattern_matches': detection_result.matches,
|
||
|
|
'recommended_mcp_servers': detection_result.recommended_mcp_servers,
|
||
|
|
'mcp_activation_plan': mcp_plan,
|
||
|
|
'routing_confidence': detection_result.confidence_score,
|
||
|
|
'optimization_opportunities': self._identify_optimization_opportunities(context, requirements)
|
||
|
|
}
|
||
|
|
|
||
|
|
def _identify_optimization_opportunities(self, context: dict, requirements: dict) -> list:
|
||
|
|
"""Identify optimization opportunities for tool execution."""
|
||
|
|
opportunities = []
|
||
|
|
|
||
|
|
# Parallel execution opportunity
|
||
|
|
if context.get('parallelizable') and context.get('file_count', 1) > 3:
|
||
|
|
opportunities.append({
|
||
|
|
'type': 'parallel_execution',
|
||
|
|
'description': 'Multi-file operation suitable for parallel processing',
|
||
|
|
'estimated_speedup': min(context.get('file_count', 1) * 0.3, 2.0)
|
||
|
|
})
|
||
|
|
|
||
|
|
# Caching opportunity
|
||
|
|
if context.get('cache_opportunity'):
|
||
|
|
opportunities.append({
|
||
|
|
'type': 'result_caching',
|
||
|
|
'description': 'Analysis results can be cached for reuse',
|
||
|
|
'estimated_speedup': 1.5
|
||
|
|
})
|
||
|
|
|
||
|
|
# MCP server coordination
|
||
|
|
if len(requirements.get('mcp_server_hints', [])) > 1:
|
||
|
|
opportunities.append({
|
||
|
|
'type': 'mcp_coordination',
|
||
|
|
'description': 'Multiple MCP servers can work together',
|
||
|
|
'quality_improvement': 0.2
|
||
|
|
})
|
||
|
|
|
||
|
|
# Intelligence routing
|
||
|
|
if not requirements.get('native_tool_sufficient'):
|
||
|
|
opportunities.append({
|
||
|
|
'type': 'intelligence_routing',
|
||
|
|
'description': 'Operation benefits from MCP server intelligence',
|
||
|
|
'quality_improvement': 0.3
|
||
|
|
})
|
||
|
|
|
||
|
|
return opportunities
|
||
|
|
|
||
|
|
def _apply_routing_adaptations(self, context: dict, routing_analysis: dict) -> dict:
|
||
|
|
"""Apply learned adaptations to routing decisions."""
|
||
|
|
base_routing = {
|
||
|
|
'recommended_mcp_servers': routing_analysis['recommended_mcp_servers'],
|
||
|
|
'mcp_activation_plan': routing_analysis['mcp_activation_plan'],
|
||
|
|
'optimization_opportunities': routing_analysis['optimization_opportunities']
|
||
|
|
}
|
||
|
|
|
||
|
|
# Apply learning engine adaptations
|
||
|
|
enhanced_routing = self.learning_engine.apply_adaptations(context, base_routing)
|
||
|
|
|
||
|
|
return enhanced_routing
|
||
|
|
|
||
|
|
def _create_execution_plan(self, context: dict, enhanced_routing: dict) -> dict:
|
||
|
|
"""Create optimal execution plan for tool usage."""
|
||
|
|
plan = {
|
||
|
|
'execution_strategy': 'direct',
|
||
|
|
'mcp_servers_required': enhanced_routing.get('recommended_mcp_servers', []),
|
||
|
|
'parallel_execution': False,
|
||
|
|
'caching_enabled': False,
|
||
|
|
'fallback_strategy': 'native_tools',
|
||
|
|
'performance_optimizations': [],
|
||
|
|
'estimated_execution_time_ms': 500
|
||
|
|
}
|
||
|
|
|
||
|
|
# Determine execution strategy
|
||
|
|
if context.get('complexity_score', 0) > 0.6:
|
||
|
|
plan['execution_strategy'] = 'intelligent_routing'
|
||
|
|
elif context.get('file_count', 1) > 5:
|
||
|
|
plan['execution_strategy'] = 'parallel_coordination'
|
||
|
|
|
||
|
|
# Configure parallel execution
|
||
|
|
if context.get('parallelizable') and context.get('file_count', 1) > 3:
|
||
|
|
plan['parallel_execution'] = True
|
||
|
|
plan['performance_optimizations'].append('parallel_file_processing')
|
||
|
|
plan['estimated_execution_time_ms'] = int(plan['estimated_execution_time_ms'] * 0.6)
|
||
|
|
|
||
|
|
# Configure caching
|
||
|
|
if context.get('cache_opportunity'):
|
||
|
|
plan['caching_enabled'] = True
|
||
|
|
plan['performance_optimizations'].append('result_caching')
|
||
|
|
|
||
|
|
# Configure MCP coordination
|
||
|
|
mcp_servers = plan['mcp_servers_required']
|
||
|
|
if len(mcp_servers) > 1:
|
||
|
|
plan['coordination_strategy'] = enhanced_routing.get('mcp_activation_plan', {}).get('coordination_strategy', 'collaborative')
|
||
|
|
|
||
|
|
# Estimate execution time based on complexity
|
||
|
|
base_time = 200
|
||
|
|
complexity_multiplier = 1 + context.get('complexity_score', 0.0)
|
||
|
|
file_multiplier = 1 + (context.get('file_count', 1) - 1) * 0.1
|
||
|
|
|
||
|
|
plan['estimated_execution_time_ms'] = int(base_time * complexity_multiplier * file_multiplier)
|
||
|
|
|
||
|
|
return plan
|
||
|
|
|
||
|
|
def _configure_tool_enhancement(self, context: dict, execution_plan: dict) -> dict:
|
||
|
|
"""Configure tool enhancement based on execution plan."""
|
||
|
|
tool_config = {
|
||
|
|
'tool_name': context['tool_name'],
|
||
|
|
'enhanced_mode': execution_plan['execution_strategy'] != 'direct',
|
||
|
|
'mcp_integration': {
|
||
|
|
'enabled': len(execution_plan['mcp_servers_required']) > 0,
|
||
|
|
'servers': execution_plan['mcp_servers_required'],
|
||
|
|
'coordination_strategy': execution_plan.get('coordination_strategy', 'single_server')
|
||
|
|
},
|
||
|
|
'performance_optimization': {
|
||
|
|
'parallel_execution': execution_plan['parallel_execution'],
|
||
|
|
'caching_enabled': execution_plan['caching_enabled'],
|
||
|
|
'optimizations': execution_plan['performance_optimizations']
|
||
|
|
},
|
||
|
|
'quality_enhancement': {
|
||
|
|
'validation_enabled': context.get('session_context', {}).get('is_production', False),
|
||
|
|
'error_recovery': True,
|
||
|
|
'context_preservation': True
|
||
|
|
},
|
||
|
|
'execution_metadata': {
|
||
|
|
'estimated_time_ms': execution_plan['estimated_execution_time_ms'],
|
||
|
|
'complexity_score': context.get('complexity_score', 0.0),
|
||
|
|
'intelligence_level': self._determine_intelligence_level(context)
|
||
|
|
}
|
||
|
|
}
|
||
|
|
|
||
|
|
# Add tool-specific enhancements
|
||
|
|
tool_config.update(self._get_tool_specific_enhancements(context, execution_plan))
|
||
|
|
|
||
|
|
return tool_config
|
||
|
|
|
||
|
|
def _determine_intelligence_level(self, context: dict) -> str:
|
||
|
|
"""Determine required intelligence level for operation."""
|
||
|
|
complexity = context.get('complexity_score', 0.0)
|
||
|
|
|
||
|
|
if complexity >= 0.8:
|
||
|
|
return 'high'
|
||
|
|
elif complexity >= 0.5:
|
||
|
|
return 'medium'
|
||
|
|
elif context.get('requires_intelligence'):
|
||
|
|
return 'medium'
|
||
|
|
else:
|
||
|
|
return 'low'
|
||
|
|
|
||
|
|
def _get_tool_specific_enhancements(self, context: dict, execution_plan: dict) -> dict:
|
||
|
|
"""Get tool-specific enhancement configurations."""
|
||
|
|
tool_name = context['tool_name']
|
||
|
|
enhancements = {}
|
||
|
|
|
||
|
|
# File operation enhancements
|
||
|
|
if tool_name in ['Read', 'Write', 'Edit']:
|
||
|
|
enhancements['file_operations'] = {
|
||
|
|
'integrity_check': True,
|
||
|
|
'backup_on_write': context.get('session_context', {}).get('is_production', False),
|
||
|
|
'encoding_detection': True
|
||
|
|
}
|
||
|
|
|
||
|
|
# Multi-file operation enhancements
|
||
|
|
if tool_name in ['MultiEdit', 'Batch'] or context.get('file_count', 1) > 3:
|
||
|
|
enhancements['multi_file_operations'] = {
|
||
|
|
'transaction_mode': True,
|
||
|
|
'rollback_capability': True,
|
||
|
|
'progress_tracking': True
|
||
|
|
}
|
||
|
|
|
||
|
|
# Analysis operation enhancements
|
||
|
|
if tool_name in ['Analyze', 'Debug', 'Search']:
|
||
|
|
enhancements['analysis_operations'] = {
|
||
|
|
'deep_context_analysis': context.get('complexity_score', 0.0) > 0.5,
|
||
|
|
'semantic_understanding': 'serena' in execution_plan['mcp_servers_required'],
|
||
|
|
'pattern_recognition': True
|
||
|
|
}
|
||
|
|
|
||
|
|
# Build/Implementation enhancements
|
||
|
|
if tool_name in ['Build', 'Implement', 'Generate']:
|
||
|
|
enhancements['build_operations'] = {
|
||
|
|
'framework_integration': 'context7' in execution_plan['mcp_servers_required'],
|
||
|
|
'component_generation': 'magic' in execution_plan['mcp_servers_required'],
|
||
|
|
'quality_validation': True
|
||
|
|
}
|
||
|
|
|
||
|
|
return enhancements
|
||
|
|
|
||
|
|
def _calculate_efficiency_score(self, context: dict, execution_time_ms: float) -> float:
|
||
|
|
"""Calculate efficiency score for the routing decision."""
|
||
|
|
# Base efficiency is inverse of execution time relative to target
|
||
|
|
time_efficiency = min(self.performance_target_ms / max(execution_time_ms, 1), 1.0)
|
||
|
|
|
||
|
|
# Complexity handling efficiency
|
||
|
|
complexity = context.get('complexity_score', 0.0)
|
||
|
|
complexity_efficiency = 1.0 - (complexity * 0.3) # Some complexity is expected
|
||
|
|
|
||
|
|
# Resource utilization efficiency
|
||
|
|
resource_usage = context.get('resource_state', {}).get('usage_percent', 0)
|
||
|
|
resource_efficiency = 1.0 - max(resource_usage - 70, 0) / 100.0
|
||
|
|
|
||
|
|
# Weighted efficiency score
|
||
|
|
efficiency_score = (time_efficiency * 0.4 +
|
||
|
|
complexity_efficiency * 0.3 +
|
||
|
|
resource_efficiency * 0.3)
|
||
|
|
|
||
|
|
return max(min(efficiency_score, 1.0), 0.0)
|
||
|
|
|
||
|
|
def _record_tool_learning(self, context: dict, tool_config: dict):
|
||
|
|
"""Record tool usage for learning purposes."""
|
||
|
|
self.learning_engine.record_learning_event(
|
||
|
|
LearningType.OPERATION_PATTERN,
|
||
|
|
AdaptationScope.USER,
|
||
|
|
context,
|
||
|
|
{
|
||
|
|
'tool_name': context['tool_name'],
|
||
|
|
'mcp_servers_used': tool_config.get('mcp_integration', {}).get('servers', []),
|
||
|
|
'execution_strategy': tool_config.get('execution_metadata', {}).get('intelligence_level', 'low'),
|
||
|
|
'optimizations_applied': tool_config.get('performance_optimization', {}).get('optimizations', [])
|
||
|
|
},
|
||
|
|
0.8, # Assume good effectiveness (will be updated later)
|
||
|
|
0.7, # Medium confidence until validated
|
||
|
|
{'hook': 'pre_tool_use', 'version': '1.0'}
|
||
|
|
)
|
||
|
|
|
||
|
|
def _create_fallback_tool_config(self, tool_request: dict, error: str) -> dict:
|
||
|
|
"""Create fallback tool configuration on error."""
|
||
|
|
return {
|
||
|
|
'tool_name': tool_request.get('tool_name', 'unknown'),
|
||
|
|
'enhanced_mode': False,
|
||
|
|
'fallback_mode': True,
|
||
|
|
'error': error,
|
||
|
|
'mcp_integration': {
|
||
|
|
'enabled': False,
|
||
|
|
'servers': [],
|
||
|
|
'coordination_strategy': 'none'
|
||
|
|
},
|
||
|
|
'performance_optimization': {
|
||
|
|
'parallel_execution': False,
|
||
|
|
'caching_enabled': False,
|
||
|
|
'optimizations': []
|
||
|
|
},
|
||
|
|
'performance_metrics': {
|
||
|
|
'routing_time_ms': 0,
|
||
|
|
'target_met': False,
|
||
|
|
'error_occurred': True
|
||
|
|
}
|
||
|
|
}
|
||
|
|
|
||
|
|
|
||
|
|
def main():
|
||
|
|
"""Main hook execution function."""
|
||
|
|
try:
|
||
|
|
# Read tool request from stdin
|
||
|
|
tool_request = json.loads(sys.stdin.read())
|
||
|
|
|
||
|
|
# Initialize and run hook
|
||
|
|
hook = PreToolUseHook()
|
||
|
|
result = hook.process_tool_use(tool_request)
|
||
|
|
|
||
|
|
# Output result as JSON
|
||
|
|
print(json.dumps(result, indent=2))
|
||
|
|
|
||
|
|
except Exception as e:
|
||
|
|
# Output error as JSON
|
||
|
|
error_result = {
|
||
|
|
'enhanced_mode': False,
|
||
|
|
'error': str(e),
|
||
|
|
'fallback_mode': True
|
||
|
|
}
|
||
|
|
print(json.dumps(error_result, indent=2))
|
||
|
|
sys.exit(1)
|
||
|
|
|
||
|
|
|
||
|
|
if __name__ == "__main__":
|
||
|
|
main()
|