atc-drug-classification

name	atc_drug_classification
description	ATC Drug Classification Lookup - Look up drug in ATC classification: ChEMBL ATC class, FDA drug info, PubChem compound, and mechanism of action. Use this skill for pharmacology tasks involving get atc class by level5 get mechanism of action by drug name get compound by name get drug by name. Combines 4 tools from 3 SCP server(s).

ATC Drug Classification Lookup

Discipline: Pharmacology | Tools Used: 4 | Servers: 3

Description

Look up drug in ATC classification: ChEMBL ATC class, FDA drug info, PubChem compound, and mechanism of action.

Tools Used

• get_atc_class_by_level5 from chembl-server (streamable-http) - https://scp.intern-ai.org.cn/api/v1/mcp/4/Origene-ChEMBL
• get_mechanism_of_action_by_drug_name from fda-drug-server (streamable-http) - https://scp.intern-ai.org.cn/api/v1/mcp/14/Origene-FDADrug
• get_compound_by_name from pubchem-server (streamable-http) - https://scp.intern-ai.org.cn/api/v1/mcp/8/Origene-PubChem
• get_drug_by_name from chembl-server (streamable-http) - https://scp.intern-ai.org.cn/api/v1/mcp/4/Origene-ChEMBL

Workflow

1. Get ATC classification
2. Get mechanism of action
3. Get PubChem compound
4. Get ChEMBL drug data

Test Case

Input

{
    "atc_code": "C10AA05",
    "drug_name": "atorvastatin"
}

Expected Steps

1. Get ATC classification
2. Get mechanism of action
3. Get PubChem compound
4. Get ChEMBL drug data

Usage Example

Note: Replace sk-b04409a1-b32b-4511-9aeb-22980abdc05c with your own SCP Hub API Key. You can obtain one from the SCP Platform.

import asyncio
import json
from contextlib import AsyncExitStack
from mcp import ClientSession
from mcp.client.streamable_http import streamablehttp_client
from mcp.client.sse import sse_client

SERVERS = {
    "chembl-server": "https://scp.intern-ai.org.cn/api/v1/mcp/4/Origene-ChEMBL",
    "fda-drug-server": "https://scp.intern-ai.org.cn/api/v1/mcp/14/Origene-FDADrug",
    "pubchem-server": "https://scp.intern-ai.org.cn/api/v1/mcp/8/Origene-PubChem"
}

async def connect(url, stack):
    transport = streamablehttp_client(url=url, headers={"SCP-HUB-API-KEY": "sk-b04409a1-b32b-4511-9aeb-22980abdc05c"})
    read, write, _ = await stack.enter_async_context(transport)
    ctx = ClientSession(read, write)
    session = await stack.enter_async_context(ctx)
    await session.initialize()
    return session

def parse(result):
    try:
        if hasattr(result, 'content') and result.content:
            c = result.content[0]
            if hasattr(c, 'text'):
                try: return json.loads(c.text)
                except: return c.text
        return str(result)
    except: return str(result)

async def main():
    async with AsyncExitStack() as stack:
        # Connect to required servers
        sessions = {}
        sessions["chembl-server"] = await connect("https://scp.intern-ai.org.cn/api/v1/mcp/4/Origene-ChEMBL", stack)
        sessions["fda-drug-server"] = await connect("https://scp.intern-ai.org.cn/api/v1/mcp/14/Origene-FDADrug", stack)
        sessions["pubchem-server"] = await connect("https://scp.intern-ai.org.cn/api/v1/mcp/8/Origene-PubChem", stack)

        # Execute workflow steps
        # Step 1: Get ATC classification
        result_1 = await sessions["chembl-server"].call_tool("get_atc_class_by_level5", arguments={})
        data_1 = parse(result_1)
        print(f"Step 1 result: {json.dumps(data_1, indent=2, ensure_ascii=False)[:500]}")

        # Step 2: Get mechanism of action
        result_2 = await sessions["fda-drug-server"].call_tool("get_mechanism_of_action_by_drug_name", arguments={})
        data_2 = parse(result_2)
        print(f"Step 2 result: {json.dumps(data_2, indent=2, ensure_ascii=False)[:500]}")

        # Step 3: Get PubChem compound
        result_3 = await sessions["pubchem-server"].call_tool("get_compound_by_name", arguments={})
        data_3 = parse(result_3)
        print(f"Step 3 result: {json.dumps(data_3, indent=2, ensure_ascii=False)[:500]}")

        # Step 4: Get ChEMBL drug data
        result_4 = await sessions["chembl-server"].call_tool("get_drug_by_name", arguments={})
        data_4 = parse(result_4)
        print(f"Step 4 result: {json.dumps(data_4, indent=2, ensure_ascii=False)[:500]}")

        # Cleanup
        print("Workflow complete!")

if __name__ == "__main__":
    asyncio.run(main())