| 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
- Get clinical studies info
- Get contraindications
- Get pregnancy effects
- Get geriatric use info
Test Case
Input
{
"drug_name": "methotrexate"
}
Expected Steps
- Get clinical studies info
- Get contraindications
- Get pregnancy effects
- 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:
sessions = {}
sessions["fda-drug-server"] = await connect("https://scp.intern-ai.org.cn/api/v1/mcp/14/Origene-FDADrug", stack)
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]}")
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]}")
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]}")
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]}")
print("Workflow complete!")
if __name__ == "__main__":
asyncio.run(main())