Document Automation MCP Server

""" Enhanced Analyzer Integration Integrates all specialized analyzers (MCP, project classification, etc.) into the main analysis workflow. """ import logging from typing import Dict, List, Any, Optional from pathlib import Path from ..parsers.mcp_analyzer import MCPServerAnalyzer, MCPServerInfo from .project_classifier import ProjectClassifier, ProjectClassification logger = logging.getLogger(__name__) class EnhancedAnalyzer: """ Enhanced analyzer that uses specialized analyzers based on project type. """ def __init__(self): self.mcp_analyzer = MCPServerAnalyzer() self.project_classifier = ProjectClassifier() def analyze( self, codebase_path: str, file_contents: Dict[str, str], dependencies: List[Dict[str, Any]], project_structure: Dict[str, Any] ) -> Dict[str, Any]: """ Perform enhanced analysis with specialized analyzers. Args: codebase_path: Root path of the codebase file_contents: Dictionary mapping file paths to contents dependencies: List of project dependencies project_structure: Project directory structure Returns: Enhanced analysis results including classification and specialized analysis """ logger.info("Starting enhanced analysis...") results = { 'codebase_path': codebase_path, 'classification': None, 'mcp_server_info': None, 'specialized_analysis': {} } # Step 1: Classify the project classification = self.project_classifier.classify( file_contents=file_contents, dependencies=dependencies, project_structure=project_structure ) results['classification'] = classification.to_dict() logger.info( f"Project classified as: {classification.primary_type} " f"(confidence: {classification.confidence:.2f})" ) # Step 2: Run specialized analyzers based on classification if classification.primary_type == 'mcp_server': logger.info("Running MCP server analysis...") mcp_info = self.mcp_analyzer.analyze(codebase_path, file_contents) if mcp_info: results['mcp_server_info'] = mcp_info.to_dict() logger.info(f"Found {len(mcp_info.tools)} MCP tools") # Step 3: Additional specialized analysis for other types elif classification.primary_type == 'rest_api': # Future: Add REST API analyzer results['specialized_analysis']['rest_api'] = self._analyze_rest_api( file_contents ) elif classification.primary_type == 'web_scraper': # Future: Add scraper analyzer results['specialized_analysis']['scraper'] = self._analyze_scraper( file_contents ) return results def _analyze_rest_api(self, file_contents: Dict[str, str]) -> Dict[str, Any]: """Placeholder for REST API analysis.""" # TODO: Implement endpoint extraction return { 'endpoints': [], 'authentication': None } def _analyze_scraper(self, file_contents: Dict[str, str]) -> Dict[str, Any]: """Placeholder for scraper analysis.""" # TODO: Implement scraper pattern analysis return { 'target_sites': [], 'extraction_methods': [] } def perform_enhanced_analysis( codebase_path: str, file_contents: Dict[str, str], dependencies: List[Dict[str, Any]], project_structure: Dict[str, Any] ) -> Dict[str, Any]: """ Convenience function for enhanced analysis. Args: codebase_path: Root path of the codebase file_contents: Dictionary mapping file paths to contents dependencies: List of project dependencies project_structure: Project directory structure Returns: Enhanced analysis results """ analyzer = EnhancedAnalyzer() return analyzer.analyze(codebase_path, file_contents, dependencies, project_structure)