MCP Code Expert System

""" Martin Fowler Expert Implementation """ from typing import Dict, List, Any, Optional import mcp.types as types from experts import CodeReviewRequest, CodeReviewResponse from knowledge_graph import KnowledgeGraph class MartinFowlerExpert: """ Expert implementation for Martin Fowler Provides code reviews focused on refactoring opportunities """ def __init__(self, knowledge_graph: KnowledgeGraph, ollama_service): """ Initialize the Martin Fowler expert Args: knowledge_graph: The knowledge graph instance ollama_service: The Ollama service instance """ self.knowledge_graph = knowledge_graph self.ollama_service = ollama_service @property def name(self) -> str: """Get the expert's name""" return "Martin Fowler" @property def tool_name(self) -> str: """Get the name of the tool for this expert""" return "ask_martin" @property def description(self) -> str: """Get the description of this expert""" return "Software design and refactoring expert" @property def tool_description(self) -> str: """Get the description of the tool for this expert""" return "Ask Martin Fowler to review your code and suggest refactorings" @property def input_schema(self) -> Dict[str, Any]: """Get the input schema for this expert's tool""" return { "type": "object", "required": ["code"], "properties": { "code": { "type": "string", "description": "The code to review" }, "description": { "type": "string", "description": "Description of what the code does" }, "language": { "type": "string", "description": "The programming language" }, "storeInGraph": { "type": "boolean", "description": "Whether to store the review in the knowledge graph", "default": True } } } async def review_code(self, request: CodeReviewRequest) -> CodeReviewResponse: """ Review code according to Martin Fowler's refactoring principles Args: request: The code review request Returns: The code review response """ print(f"[MartinFowlerExpert] Reviewing code: {request.code[:50]}...") # Get review from Ollama result = self.ollama_service.get_martin_fowler_review( code=request.code, language=request.language, description=request.description ) print(f"[MartinFowlerExpert] Review result: {result['rating']}/5") # Create response response = CodeReviewResponse( review=result["review"], suggestions=result["suggestions"], rating=result["rating"] ) # Store in knowledge graph if requested if request.storeInGraph: self._store_review_in_graph(request, response) return response def _store_review_in_graph(self, request: CodeReviewRequest, response: CodeReviewResponse) -> None: """ Store a code review in the knowledge graph Args: request: The code review request response: The code review response """ # Create code node code_name = f"code-{len(self.knowledge_graph.nodes) + 1}" self.knowledge_graph.add_node( code_name, "CodeSnippet", { "code": request.code, "description": request.description, "language": request.language, } ) # Create review node review_name = f"martin-review-{len(self.knowledge_graph.nodes) + 1}" self.knowledge_graph.add_node( review_name, "CodeReview", { "review": response.review, "suggestions": response.suggestions, "rating": response.rating, "reviewer": self.name, } ) # Ensure expert exists expert_name = self.name expert_node = self.knowledge_graph.get_node(expert_name) if not expert_node: self.knowledge_graph.add_node( expert_name, "Expert", { "expertise": "Refactoring", "description": self.description } ) # Add relationships self.knowledge_graph.add_edge( review_name, code_name, "reviews" ) self.knowledge_graph.add_edge( expert_name, review_name, "authored" )