clinical-trial-drug-profile

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Clinical Trial Drug Profiling - Profile drug for clinical trials: FDA clinical studies, contraindications, pregnancy info, and geriatric use. Use this skill for clinical research tasks involving get clinical studies info by drug name get contraindications by drug name get pregnancy effects info by drug name get geriatric use info by drug name. Combines 4 tools from 1 SCP server(s).

SpectrAI-Initiative By SpectrAI-Initiative schedule Updated 4/2/2026

name: clinical_trial_drug_profile description: "Clinical Trial Drug Profiling - Profile drug for clinical trials: FDA clinical studies, contraindications, pregnancy info, and geriatric use. Use this skill for clinical research tasks involving get clinical studies info by drug name get contraindications by drug name get pregnancy effects info by drug name get geriatric use info by drug name. Combines 4 tools from 1 SCP server(s)."

Clinical Trial Drug Profiling

Discipline: Clinical Research | Tools Used: 4 | Servers: 1

Description

Profile drug for clinical trials: FDA clinical studies, contraindications, pregnancy info, and geriatric use.

Tools Used

  • get_clinical_studies_info_by_drug_name from fda-drug-server (streamable-http) - https://scp.intern-ai.org.cn/api/v1/mcp/14/Origene-FDADrug
  • get_contraindications_by_drug_name from fda-drug-server (streamable-http) - https://scp.intern-ai.org.cn/api/v1/mcp/14/Origene-FDADrug
  • get_pregnancy_effects_info_by_drug_name from fda-drug-server (streamable-http) - https://scp.intern-ai.org.cn/api/v1/mcp/14/Origene-FDADrug
  • get_geriatric_use_info_by_drug_name from fda-drug-server (streamable-http) - https://scp.intern-ai.org.cn/api/v1/mcp/14/Origene-FDADrug

Workflow

  1. Get clinical studies info
  2. Get contraindications
  3. Get pregnancy effects
  4. Get geriatric use info

Test Case

Input

{
    "drug_name": "methotrexate"
}

Expected Steps

  1. Get clinical studies info
  2. Get contraindications
  3. Get pregnancy effects
  4. Get geriatric use info

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"
}

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)

        # Execute workflow steps
        # Step 1: Get clinical studies info
        result_1 = await sessions["fda-drug-server"].call_tool("get_clinical_studies_info_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 contraindications
        result_2 = await sessions["fda-drug-server"].call_tool("get_contraindications_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 pregnancy effects
        result_3 = await sessions["fda-drug-server"].call_tool("get_pregnancy_effects_info_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 geriatric use info
        result_4 = await sessions["fda-drug-server"].call_tool("get_geriatric_use_info_by_drug_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())
Install via CLI
npx skills add https://github.com/SpectrAI-Initiative/InnoClaw --skill clinical-trial-drug-profile
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