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pharmacokinetics_profile

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Pharmacokinetics Profile Builder - Build a PK profile: FDA pharmacokinetics, clinical pharmacology, dosage info, and molecular properties. Use this skill for pharmacology tasks involving get pharmacokinetics by drug name get clinical pharmacology by drug name get dosage and storage information by drug name get compound by name. Combines 4 tools from 2 SCP server(s).

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Pharmacokinetics Profile Builder

Discipline: Pharmacology | Tools Used: 4 | Servers: 2

Description

Build a PK profile: FDA pharmacokinetics, clinical pharmacology, dosage info, and molecular properties.

Tools Used

  • get_pharmacokinetics_by_drug_name from fda-drug-server (streamable-http) - https://scp.intern-ai.org.cn/api/v1/mcp/14/Origene-FDADrug
  • get_clinical_pharmacology_by_drug_name from fda-drug-server (streamable-http) - https://scp.intern-ai.org.cn/api/v1/mcp/14/Origene-FDADrug
  • get_dosage_and_storage_information_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

Workflow

  1. Get PK data from FDA
  2. Get clinical pharmacology
  3. Get dosage info
  4. Get molecular structure from PubChem

Test Case

Input

{
    "drug_name": "atorvastatin"
}

Expected Steps

  1. Get PK data from FDA
  2. Get clinical pharmacology
  3. Get dosage info
  4. Get molecular structure from PubChem

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 = {
    "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["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 PK data from FDA
        result_1 = await sessions["fda-drug-server"].call_tool("get_pharmacokinetics_by_drug_name", arguments={})
        data_1 = parse(result_1)
        print(f"Step 1 result: {json.dumps(data_1, indent=2, ensure_ascii=False)[:500]}")

        # Step 2: Get clinical pharmacology
        result_2 = await sessions["fda-drug-server"].call_tool("get_clinical_pharmacology_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 dosage info
        result_3 = await sessions["fda-drug-server"].call_tool("get_dosage_and_storage_information_by_drug_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 molecular structure from PubChem
        result_4 = await sessions["pubchem-server"].call_tool("get_compound_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())