import json
from mcp.client.streamable_http import streamablehttp_client
from mcp import ClientSession
class DrugSDAClient:    
    def __init__(self, server_url: str):
        self.server_url = server_url
        self.session = None
        
    async def connect(self):
        print(f"server url: {self.server_url}")
        try:
            self.transport = streamablehttp_client(
                url=self.server_url,
                headers={"SCP-HUB-API-KEY": "sk-a0033dde-b3cd-413b-adbe-980bc78d6126"}
            )
            self.read, self.write, self.get_session_id = await self.transport.__aenter__()
            
            self.session_ctx = ClientSession(self.read, self.write)
            self.session = await self.session_ctx.__aenter__()
            await self.session.initialize()
            session_id = self.get_session_id()
            
            print(f"✓ connect success")
            return True
            
        except Exception as e:
            print(f"✗ connect failure: {e}")
            import traceback
            traceback.print_exc()
            return False
    
    async def disconnect(self):
        try:
            if self.session:
                await self.session_ctx.__aexit__(None, None, None)
            if hasattr(self, 'transport'):
                await self.transport.__aexit__(None, None, None)
            print("✓ already disconnect")
        except Exception as e:
            print(f"✗ disconnect error: {e}")
    
    def parse_result(self, result):
        try:
            if hasattr(result, 'content') and result.content:
                content = result.content[0]
                if hasattr(content, 'text'):
                    return json.loads(content.text)
            return str(result)
        except Exception as e:
            return {"error": f"parse error: {e}", "raw": str(result)}

Generate new molecules sampling from the input molecule using different priors ('similarity': broad exploration, 'medium_similarity': balanced exploration, 'high_similarity': conservative optimization, 'scaffold': strict scaffold preservation, 'scaffold_generic': generic scaffold preservation, 'mmp': MMP-style local modifications).
Args:
    smiles (str): Input SMILES string
    n (int): Number of molecules for sampling
    min_similarity (float): Minimum similarity threshold, default is 0.6
    prior_type (str): Prior type for generation, options: ['scaffold_generic', 'scaffold', 'mmp', 'similarity', 'high_similarity', 'medium_similarity'], default is 'similarity'
    lipinski (bool): Whether to apply Lipinski's rule of five filtering, default is True
    filter_preset (str): Filter preset, options: ['none', 'minimal', 'default', 'strict'], default is 'default'
Return:
    status (str): success/error
    msg (str): message
    save_smiles_file (str): Path to the saved SMILES file
    output_smiles_list (List[str]): List of generated SMILES strings

client = DrugSDAClient("https://scp.intern-ai.org.cn/api/v1/mcp/2/DrugSDA-Tool")
if not await client.connect():
    print("connection failed")
    return
response = await client.session.call_tool(
    "reinvent_mol2mol_sampling",
    arguments={
        "smiles": smiles,
        "n": n,
        "min_similarity": min_similarity,
        "prior_type": prior_type,
        "lipinski": True,
        "filter_preset": filter_type
    }
)
result = client.parse_result(response)
output_smiles_list = result["output_smiles_list"]
await client.disconnect()