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"""
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"] |