skills/clinical_trial_drug_profile/SKILL.md
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).
npx skillsauth add InternScience/scp clinical_trial_drug_profileInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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Discipline: Clinical Research | Tools Used: 4 | Servers: 1
Profile drug for clinical trials: FDA clinical studies, contraindications, pregnancy info, and geriatric use.
get_clinical_studies_info_by_drug_name from fda-drug-server (streamable-http) - https://scp.intern-ai.org.cn/api/v1/mcp/14/Origene-FDADrugget_contraindications_by_drug_name from fda-drug-server (streamable-http) - https://scp.intern-ai.org.cn/api/v1/mcp/14/Origene-FDADrugget_pregnancy_effects_info_by_drug_name from fda-drug-server (streamable-http) - https://scp.intern-ai.org.cn/api/v1/mcp/14/Origene-FDADrugget_geriatric_use_info_by_drug_name from fda-drug-server (streamable-http) - https://scp.intern-ai.org.cn/api/v1/mcp/14/Origene-FDADrug{
"drug_name": "methotrexate"
}
Note: Replace
<YOUR_SCP_HUB_API_KEY>with your own SCP Hub API Key. You can obtain one from the SCP Platform.
import asyncio
import json
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, transport_type):
transport = streamablehttp_client(url=url, headers={"SCP-HUB-API-KEY": "<YOUR_SCP_HUB_API_KEY>"})
read, write, _ = await transport.__aenter__()
ctx = ClientSession(read, write)
session = await ctx.__aenter__()
await session.initialize()
return session, ctx, transport
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():
# Connect to required servers
sessions = {}
sessions["fda-drug-server"], _, _ = await connect("https://scp.intern-ai.org.cn/api/v1/mcp/14/Origene-FDADrug", "streamable-http")
# 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())
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