Substance Toxicology Report - Toxicology report: PubChem substance data, FDA toxicology, carcinogenicity data, and environmental warnings. Use this skill for toxicology tasks involving get substance by name get nonclinical toxicology info by drug name get carcinogenic mutagenic fertility impairment info by drug name get environmental warning by drug name. Combines 4 tools from 2 SCP server(s).
Discipline: Toxicology | Tools Used: 4 | Servers: 2
Toxicology report: PubChem substance data, FDA toxicology, carcinogenicity data, and environmental warnings.
get_substance_by_name from pubchem-server (streamable-http) - https://scp.intern-ai.org.cn/api/v1/mcp/8/Origene-PubChemget_nonclinical_toxicology_info_by_drug_name from fda-drug-server (streamable-http) - https://scp.intern-ai.org.cn/api/v1/mcp/14/Origene-FDADrugget_carcinogenic_mutagenic_fertility_impairment_info_by_drug_name from fda-drug-server (streamable-http) - https://scp.intern-ai.org.cn/api/v1/mcp/14/Origene-FDADrugget_environmental_warning_by_drug_namefda-drug-serverhttps://scp.intern-ai.org.cn/api/v1/mcp/14/Origene-FDADrug{
"substance": "benzene",
"drug_name": "benzene"
}
Note: Replace
sk-b04409a1-b32b-4511-9aeb-22980abdc05cwith 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 = {
"pubchem-server": "https://scp.intern-ai.org.cn/api/v1/mcp/8/Origene-PubChem",
"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["pubchem-server"] = await connect("https://scp.intern-ai.org.cn/api/v1/mcp/8/Origene-PubChem", stack)
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 PubChem substance data
result_1 = await sessions["pubchem-server"].call_tool("get_substance_by_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 FDA nonclinical toxicology
result_2 = await sessions["fda-drug-server"].call_tool("get_nonclinical_toxicology_info_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 carcinogenicity data
result_3 = await sessions["fda-drug-server"].call_tool("get_carcinogenic_mutagenic_fertility_impairment_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 environmental warnings
result_4 = await sessions["fda-drug-server"].call_tool("get_environmental_warning_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())