""" FhirFlame MCP Server - Medical Document Intelligence Platform MCP Server with 2 perfect tools: process_medical_document & validate_fhir_bundle CodeLlama 13B-instruct + RTX 4090 GPU optimization """ import asyncio import json import time from typing import Dict, List, Any, Optional from .monitoring import monitor # Use correct MCP imports for fast initial testing try: from mcp.server import Server from mcp.types import Tool, TextContent from mcp import CallToolRequest except ImportError: # Mock for testing if MCP not available class Server: def __init__(self, name): pass class Tool: def __init__(self, **kwargs): pass class TextContent: def __init__(self, **kwargs): pass class CallToolRequest: pass class FhirFlameMCPServer: """MCP Server for medical document processing with CodeLlama 13B""" def __init__(self): """Initialize FhirFlame MCP Server""" self.name = "fhirflame" self.server = None # Will be initialized when needed self._tool_definitions = self._register_tools() self.tools = [tool["name"] for tool in self._tool_definitions] # Tool names for compatibility def _register_tools(self) -> List[Dict[str, Any]]: """Register the 2 perfect MCP tools""" return [ { "name": "process_medical_document", "description": "Process medical documents using CodeLlama 13B-instruct on RTX 4090", "parameters": { "document_content": { "type": "string", "description": "Medical document text to process", "required": True }, "document_type": { "type": "string", "description": "Type of medical document", "enum": ["discharge_summary", "clinical_note", "lab_report"], "default": "clinical_note", "required": False }, "extract_entities": { "type": "boolean", "description": "Whether to extract medical entities", "default": True, "required": False } } }, { "name": "validate_fhir_bundle", "description": "Validate FHIR R4 bundles for healthcare compliance", "parameters": { "fhir_bundle": { "type": "object", "description": "FHIR R4 bundle to validate", "required": True }, "validation_level": { "type": "string", "description": "Validation strictness level", "enum": ["basic", "standard", "healthcare_grade"], "default": "standard", "required": False } } } ] def get_tools(self) -> List[Dict[str, Any]]: """Get available MCP tools""" return self._tool_definitions def get_tool(self, name: str) -> Dict[str, Any]: """Get a specific tool by name""" for tool in self._tool_definitions: if tool["name"] == name: return tool raise ValueError(f"Tool not found: {name}") async def call_tool(self, name: str, arguments: Dict[str, Any]) -> Dict[str, Any]: """Call MCP tool by name""" if name == "process_medical_document": return await self._process_medical_document(arguments) elif name == "validate_fhir_bundle": return await self._validate_fhir_bundle(arguments) else: raise ValueError(f"Unknown tool: {name}") async def _process_medical_document(self, args: Dict[str, Any]) -> Dict[str, Any]: """Process medical document with CodeLlama 13B""" from .codellama_processor import CodeLlamaProcessor medical_text = args.get("document_content", "") document_type = args.get("document_type", "clinical_note") extract_entities = args.get("extract_entities", True) # Edge case: Handle empty document content if not medical_text or medical_text.strip() == "": return { "success": False, "error": "Empty document content provided. Cannot process empty medical documents.", "processing_metadata": { "model_used": "codellama:13b-instruct", "gpu_used": "RTX_4090", "vram_used": "0GB", "processing_time": 0.0 } } # Real CodeLlama processing implementation processor = CodeLlamaProcessor() try: # Process the medical document with FHIR bundle generation processing_result = await processor.process_document( medical_text, document_type=document_type, extract_entities=extract_entities, generate_fhir=True ) return { "success": True, "processing_metadata": processing_result.get("metadata", {}), "extraction_results": processing_result.get("extraction_results", {}), "extracted_data": processing_result.get("extracted_data", "{}"), "entities_extracted": extract_entities, "fhir_bundle": processing_result.get("fhir_bundle", {}) } except Exception as e: return { "success": False, "error": f"Processing failed: {str(e)}", "processing_metadata": { "model_used": "codellama:13b-instruct", "gpu_used": "RTX_4090", "vram_used": "0GB", "processing_time": 0.0 } } async def _validate_fhir_bundle(self, args: Dict[str, Any]) -> Dict[str, Any]: """Validate FHIR R4 bundle""" from .fhir_validator import FhirValidator fhir_bundle = args.get("fhir_bundle", {}) validation_level = args.get("validation_level", "standard") # Edge case: Handle empty or invalid bundle if not fhir_bundle or not isinstance(fhir_bundle, dict): return { "success": False, "error": "Invalid or empty FHIR bundle provided", "validation_results": { "is_valid": False, "compliance_score": 0.0, "validation_level": validation_level, "fhir_version": "R4" }, "compliance_summary": { "fhir_r4_compliant": False, "hipaa_ready": False, "terminology_validated": False, "structure_validated": False }, "compliance_score": 0.0, "validation_errors": ["Bundle is empty or invalid"], "warnings": [], "healthcare_grade": False } # Real FHIR validation implementation validator = FhirValidator() try: # Validate the FHIR bundle using sync method validation_result = validator.validate_bundle(fhir_bundle, validation_level=validation_level) return { "success": True, "validation_results": { "is_valid": validation_result["is_valid"], "compliance_score": validation_result["compliance_score"], "validation_level": validation_result["validation_level"], "fhir_version": validation_result["fhir_version"] }, "compliance_summary": { "fhir_r4_compliant": validation_result["fhir_r4_compliant"], "hipaa_ready": validation_result["hipaa_compliant"], "terminology_validated": validation_result["medical_coding_validated"], "structure_validated": validation_result["is_valid"] }, "compliance_score": validation_result["compliance_score"], "validation_errors": validation_result["errors"], "warnings": validation_result["warnings"], "healthcare_grade": validation_level == "healthcare_grade" } except Exception as e: return { "success": False, "error": f"Validation failed: {str(e)}", "validation_results": { "is_valid": False, "compliance_score": 0.0, "validation_level": validation_level, "fhir_version": "R4" }, "compliance_summary": { "fhir_r4_compliant": False, "hipaa_ready": False, "terminology_validated": False, "structure_validated": False }, "compliance_score": 0.0, "validation_errors": [f"Validation error: {str(e)}"], "warnings": [], "healthcare_grade": False } async def run_server(self, port: int = 8000): """Run MCP server""" # This will be implemented with actual MCP server logic pass # Make class available for import __all__ = ["FhirFlameMCPServer"]