Kotlin MCP Server

#!/usr/bin/env python3 """ Intelligent Base Classes for LSP - like Tool Enhancement This module provides base classes and utilities that enhance all MCP tools with intelligent, LSP - like capabilities for Kotlin development. """ import asyncio import json from abc import ABC, abstractmethod from dataclasses import asdict, dataclass from pathlib import Path from typing import Any, Dict, List, Optional, Union # Import our existing intelligence modules from ai.intelligent_analysis import CodeIssue, KotlinAnalyzer, RefactoringType from ai.llm_integration import CodeGenerationRequest, CodeType, LLMIntegration from tools.intelligent_navigation import IntelligentSymbolNavigation from tools.intelligent_refactoring import IntelligentRefactoringTools @dataclass class IntelligentToolContext: """Context information for intelligent tool execution.""" project_path: str tool_name: str current_file: Optional[str] = None selection_start: Optional[int] = None selection_end: Optional[int] = None user_intent: Optional[str] = None project_symbols: Optional[Dict[str, Any]] = None project_analysis: Optional[Dict[str, Any]] = None def to_dict(self) -> Dict[str, Any]: return asdict(self) @dataclass class IntelligentToolResult: """Enhanced result from intelligent tool execution.""" success: bool primary_result: Any intelligence_insights: Optional[Dict[str, Any]] = None refactoring_suggestions: Optional[List[Dict[str, Any]]] = None code_issues: Optional[List[Dict[str, Any]]] = None symbol_information: Optional[Dict[str, Any]] = None performance_metrics: Optional[Dict[str, Any]] = None related_files: Optional[List[str]] = None impact_analysis: Optional[Dict[str, Any]] = None def to_mcp_response(self) -> Dict[str, Any]: """Convert to MCP response format.""" content = [] # Primary result if isinstance(self.primary_result, str): content.append({"type": "text", "text": self.primary_result}) else: content.append({"type": "text", "text": json.dumps(self.primary_result, indent=2)}) # Add intelligence insights if available if self.intelligence_insights: content.append( { "type": "text", "text": f"\n🧠 **Intelligence Insights:**\n{json.dumps(self.intelligence_insights, indent = 2)}", } ) # Add refactoring suggestions if self.refactoring_suggestions: content.append( { "type": "text", "text": f"\n🔧 **Refactoring Suggestions:**\n{json.dumps(self.refactoring_suggestions, indent = 2)}", } ) # Add code issues if self.code_issues: content.append( { "type": "text", "text": f"\n⚠️ **Code Quality Issues:**\n{json.dumps(self.code_issues, indent = 2)}", } ) # Add symbol information if self.symbol_information: content.append( { "type": "text", "text": f"\n📋 **Symbol Analysis:**\n{json.dumps(self.symbol_information, indent = 2)}", } ) return {"content": content} class IntelligentToolBase(ABC): """ Base class for all intelligent MCP tools. Provides LSP - like capabilities including: - Semantic code analysis - Symbol resolution and navigation - Intelligent refactoring suggestions - Context - aware code completion - Impact analysis and safety checks """ def __init__(self, project_path: str, security_manager: Optional[Any] = None): self.project_path = Path(project_path) self.security_manager = security_manager # Initialize intelligent components self.analyzer = KotlinAnalyzer() self.llm_integration = LLMIntegration(security_manager) self.symbol_navigation = IntelligentSymbolNavigation() self.refactoring_tools = IntelligentRefactoringTools(project_path, security_manager) # Cache for project - wide analysis self._project_symbols: Optional[Dict[str, Any]] = None self._project_analysis: Optional[Dict[str, Any]] = None async def execute_with_intelligence( self, context: IntelligentToolContext, arguments: Dict[str, Any] ) -> IntelligentToolResult: """ Execute the tool with full intelligence capabilities. This method: 1. Performs semantic analysis of relevant code 2. Executes the core tool functionality 3. Provides intelligent insights and suggestions 4. Analyzes impact and safety considerations """ try: # Ensure project symbols are indexed await self._ensure_project_indexed() # Perform pre - execution analysis pre_analysis = await self._analyze_context(context) # Execute the core tool functionality core_result = await self._execute_core_functionality(context, arguments) # Perform post - execution analysis and enhancement intelligence_insights = await self._generate_intelligence_insights( context, arguments, core_result, pre_analysis ) # Generate refactoring suggestions refactoring_suggestions = await self._generate_refactoring_suggestions( context, pre_analysis ) # Analyze impact of the changes impact_analysis = await self._analyze_impact(context, core_result) return IntelligentToolResult( success=True, primary_result=core_result, intelligence_insights=intelligence_insights, refactoring_suggestions=refactoring_suggestions, code_issues=pre_analysis.get("code_issues", []), symbol_information=pre_analysis.get("symbols", {}), impact_analysis=impact_analysis, related_files=pre_analysis.get("related_files", []), ) except Exception as e: return IntelligentToolResult( success=False, primary_result=f"Tool execution failed: {str(e)}", intelligence_insights={"error": str(e)}, ) @abstractmethod async def _execute_core_functionality( self, context: IntelligentToolContext, arguments: Dict[str, Any] ) -> Any: """Execute the core functionality of the specific tool.""" pass async def _ensure_project_indexed(self) -> None: """Ensure project symbols are indexed for intelligent operations.""" if self._project_symbols is None: index_result = await self.symbol_navigation.index_project(str(self.project_path)) self._project_symbols = index_result async def _analyze_context(self, context: IntelligentToolContext) -> Dict[str, Any]: """Analyze the current context for intelligent insights.""" analysis: Dict[str, Any] = { "symbols": {}, "code_issues": [], "related_files": [], "complexity_metrics": {}, } if context.current_file and Path(context.current_file).exists(): # Analyze the current file with open(context.current_file, "r", encoding="utf-8") as f: content = f.read() file_analysis = self.analyzer.analyze_file(context.current_file, content) analysis.update(file_analysis) # Find related files based on imports and usage related_files = await self._find_related_files(context.current_file, content) analysis["related_files"] = related_files return analysis async def _generate_intelligence_insights( self, context: IntelligentToolContext, arguments: Dict[str, Any], core_result: Any, pre_analysis: Dict[str, Any], ) -> Dict[str, Any]: """Generate intelligent insights about the tool execution.""" insights = { "execution_context": { "tool_purpose": self.__class__.__name__, "project_context": str(self.project_path), "affected_files": pre_analysis.get("related_files", []), }, "code_quality_assessment": { "issues_found": len(pre_analysis.get("code_issues", [])), "symbols_analyzed": len(pre_analysis.get("symbols", [])), "complexity_score": pre_analysis.get("complexity_metrics", {}).get( "total_complexity", 0 ), }, "recommendations": await self._generate_recommendations(context, pre_analysis), } return insights async def _generate_refactoring_suggestions( self, context: IntelligentToolContext, analysis: Dict[str, Any] ) -> List[Dict[str, Any]]: """Generate context - aware refactoring suggestions.""" suggestions = [] if context.current_file: refactoring_result = await self.refactoring_tools.suggest_intelligent_refactoring( context.current_file, context.user_intent or "improve code quality", context.selection_start, context.selection_end, ) if refactoring_result.get("success"): suggestions = refactoring_result.get("suggestions", []) return suggestions async def _analyze_impact(self, context: IntelligentToolContext, result: Any) -> Dict[str, Any]: """Analyze the impact of the tool execution.""" impact = { "files_modified": [], "symbols_affected": [], "breaking_changes_risk": "low", "test_impact": "minimal", "build_impact": "none", "recommendations": [], } # This is a base implementation - specific tools can override if context.current_file: impact["files_modified"] = [context.current_file] return impact async def _find_related_files(self, current_file: str, content: str) -> List[str]: """Find files related to the current file through imports and usage.""" related = [] # Find imports import_pattern = r"import\s+([a - zA - Z0 - 9_.]+)" imports = [] for match in __import__("re").finditer(import_pattern, content): imports.append(match.group(1)) # Convert imports to potential file paths for import_path in imports: # Convert package.Class to potential file paths file_path = import_path.replace(".", "/") + ".kt" potential_file = self.project_path / "src" / "main" / "kotlin" / file_path if potential_file.exists(): related.append(str(potential_file)) return related async def _generate_recommendations( self, context: IntelligentToolContext, analysis: Dict[str, Any] ) -> List[str]: """Generate intelligent recommendations based on analysis.""" recommendations = [] # Code quality recommendations issues = analysis.get("code_issues", []) if issues: high_priority = [issue for issue in issues if issue.get("severity") == "error"] if high_priority: recommendations.append( "Address {len(high_priority)} critical code issues before proceeding" ) # Performance recommendations complexity = analysis.get("complexity_metrics", {}).get("total_complexity", 0) if complexity > 50: # Arbitrary threshold recommendations.append("Consider refactoring to reduce code complexity") # Architecture recommendations symbols = analysis.get("symbols", []) class_count = len([s for s in symbols if s.get("type") == "class"]) if class_count > 10: # Large file recommendations.append("Consider splitting this file into smaller, focused classes") return recommendations class IntelligentBuildTool(IntelligentToolBase): """Enhanced build tool with intelligent analysis.""" async def _execute_core_functionality( self, context: IntelligentToolContext, arguments: Dict[str, Any] ) -> Any: """Execute Gradle build with intelligent analysis.""" task = arguments.get("task", "assembleDebug") clean = arguments.get("clean", False) # Pre - build analysis build_analysis = await self._analyze_build_configuration() # Execute build import subprocess try: if clean: clean_result = subprocess.run( ["./gradlew", "clean"], cwd=self.project_path, capture_output=True, text=True, timeout=300, ) build_result = subprocess.run( ["./gradlew", task], cwd=self.project_path, capture_output=True, text=True, timeout=600, ) return { "build_result": { "task": task, "exit_code": build_result.returncode, "output": build_result.stdout, "errors": build_result.stderr, }, "build_analysis": build_analysis, "performance_metrics": await self._analyze_build_performance(), } except subprocess.TimeoutExpired: return {"error": "Build timed out - consider build optimization"} except Exception as e: return {"error": "Build failed: {str(e)}"} async def _analyze_build_configuration(self) -> Dict[str, Any]: """Analyze build configuration for optimization opportunities.""" build_gradle = self.project_path / "build.gradle.kts" if not build_gradle.exists(): build_gradle = self.project_path / "build.gradle" if not build_gradle.exists(): return {"error": "No build.gradle file found"} with open(build_gradle, "r") as f: content = f.read() optimization_opportunities: List[str] = [] analysis = { "kotlin_version": self._extract_kotlin_version(content), "dependencies_count": len( __import__("re").findall(r"implementation|api|compile", content) ), "build_features": self._analyze_build_features(content), "optimization_opportunities": optimization_opportunities, } # Check for optimization opportunities if "buildCache" not in content: optimization_opportunities.append("Enable build cache for faster incremental builds") if "parallel" not in content: optimization_opportunities.append("Enable parallel execution") return analysis def _extract_kotlin_version(self, content: str) -> Optional[str]: """Extract Kotlin version from build file.""" import re match = re.search(r'kotlin_version\s*=\s*["\']([^"\']+)["\']', content) if match: return match.group(1) return None def _analyze_build_features(self, content: str) -> List[str]: """Analyze enabled build features.""" features = [] feature_patterns = [ (r"dataBinding\s*{\s * enabled\s*=\s * true", "DataBinding"), (r"viewBinding\s*{\s * enabled\s*=\s * true", "ViewBinding"), (r"compose\s * true", "Jetpack Compose"), (r"buildConfig\s * true", "BuildConfig"), ] for pattern, feature in feature_patterns: if __import__("re").search(pattern, content): features.append(feature) return features async def _analyze_build_performance(self) -> Dict[str, Any]: """Analyze build performance metrics.""" # This would integrate with Gradle build scans in a real implementation return { "estimated_build_time": "30 - 60 seconds", "optimization_potential": "medium", "bottlenecks": ["annotation processing", "resource compilation"], } class IntelligentTestTool(IntelligentToolBase): """Enhanced test tool with intelligent test analysis.""" async def _execute_core_functionality( self, context: IntelligentToolContext, arguments: Dict[str, Any] ) -> Any: """Execute tests with intelligent analysis.""" test_type = arguments.get("test_type", "unit") # Pre - test analysis test_analysis = await self._analyze_test_coverage() # Execute tests import subprocess task_map = { "unit": "test", "instrumented": "connectedAndroidTest", "all": "test connectedAndroidTest", } task = task_map.get(test_type, "test") try: result = subprocess.run( ["./gradlew"] + task.split(), cwd=self.project_path, capture_output=True, text=True, timeout=900, ) return { "test_result": { "test_type": test_type, "exit_code": result.returncode, "output": result.stdout, "errors": result.stderr, }, "test_analysis": test_analysis, "coverage_report": await self._generate_coverage_insights(result.stdout), } except Exception as e: return {"error": "Test execution failed: {str(e)}"} async def _analyze_test_coverage(self) -> Dict[str, Any]: """Analyze test coverage and identify gaps.""" test_dirs = [ self.project_path / "src" / "test" / "kotlin", self.project_path / "src" / "androidTest" / "kotlin", ] test_files = [] for test_dir in test_dirs: if test_dir.exists(): test_files.extend(list(test_dir.rglob("*.kt"))) source_files = list((self.project_path / "src" / "main" / "kotlin").rglob("*.kt")) return { "total_test_files": len(test_files), "total_source_files": len(source_files), "estimated_coverage": "{min(100, (len(test_files) / max(len(source_files), 1)) * 100):.1f}%", "missing_tests": await self._identify_missing_tests(source_files, test_files), } async def _identify_missing_tests( self, source_files: List[Path], test_files: List[Path] ) -> List[str]: """Identify source files without corresponding tests.""" test_names = {f.stem.replace("Test", "").replace("Spec", "") for f in test_files} source_names = {f.stem for f in source_files} missing = [] for source_name in source_names: if source_name not in test_names and not source_name.endswith( "Activity" ): # Skip activities for now missing.append(source_name) return missing[:10] # Limit to first 10 async def _generate_coverage_insights(self, test_output: str) -> Dict[str, Any]: """Generate insights from test output.""" performance_issues: List[str] = [] insights = { "tests_run": 0, "tests_passed": 0, "tests_failed": 0, "performance_issues": performance_issues, } # Parse test output for basic metrics import re test_pattern = r"(\d+) tests? completed" match = re.search(test_pattern, test_output) if match: insights["tests_run"] = int(match.group(1)) # Look for performance issues if "SLOW" in test_output or "timeout" in test_output.lower(): performance_issues.append("Some tests are running slowly") return insights