Drug-Indication Mapping
SkillSearchDrug-Indication Mapping - Map drug indications: ChEMBL drug indications, FDA indications, OpenTargets drug associations, and literature. Use this skill for clinical informatics tasks involving get drug indication by id get indications by drug name get associated drugs by target name pubmed search. Combines 4 tools from 4 SCP server(s).
Available today. Use it from your connected AI after setup.
No other account needed.
Connect ahel once, and every AI you use reads what you have installed.
Then ask your AI: use the Drug-Indication Mapping skill
What this skill tells your AI
The instructions your AI receives, as published by internscience/scp in skills/drug_indication_mapping/SKILL.md and read by ahel’s review.
Discipline: Clinical Informatics | Tools Used: 4 | Servers: 4
Description
Map drug indications: ChEMBL drug indications, FDA indications, OpenTargets drug associations, and literature.
Tools Used
get_drug_indication_by_idfromchembl-server(streamable-http) -https://scp.intern-ai.org.cn/api/v1/mcp/4/Origene-ChEMBLget_indications_by_drug_namefromfda-drug-server(streamable-http) -https://scp.intern-ai.org.cn/api/v1/mcp/14/Origene-FDADrugget_associated_drugs_by_target_namefromopentargets-server(streamable-http) -https://scp.intern-ai.org.cn/api/v1/mcp/15/Origene-OpenTargetspubmed_searchfromsearch-server(streamable-http) -https://scp.intern-ai.org.cn/api/v1/mcp/7/Origene-Search
Workflow
- Get ChEMBL indication data
- Get FDA indications
- Get OpenTargets drug associations
- Search PubMed for evidence
Test Case
Input
{
"drug_name": "imatinib",
"drugind_id": 1
}
Expected Steps
- Get ChEMBL indication data
- Get FDA indications
- Get OpenTargets drug associations
- Search PubMed for evidence
Usage Example
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 = {
"chembl-server": "https://scp.intern-ai.org.cn/api/v1/mcp/4/Origene-ChEMBL",
"fda-drug-server": "https://scp.intern-ai.org.cn/api/v1/mcp/14/Origene-FDADrug",
"opentargets-server": "https://scp.intern-ai.org.cn/api/v1/mcp/15/Origene-OpenTargets",
"search-server": "https://scp.intern-ai.org.cn/api/v1/mcp/7/Origene-Search"
}
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["chembl-server"], _, _ = await connect("https://scp.intern-ai.org.cn/api/v1/mcp/4/Origene-ChEMBL", "streamable-http")
sessions["fda-drug-server"], _, _ = await connect("https://scp.intern-ai.org.cn/api/v1/mcp/14/Origene-FDADrug", "streamable-http")
sessions["opentargets-server"], _, _ = await connect("https://scp.intern-ai.org.cn/api/v1/mcp/15/Origene-OpenTargets", "streamable-http")
sessions["search-server"], _, _ = await connect("https://scp.intern-ai.org.cn/api/v1/mcp/7/Origene-Search", "streamable-http")
# Execute workflow steps
# Step 1: Get ChEMBL indication data
result_1 = await sessions["chembl-server"].call_tool("get_drug_indication_by_id", 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 indications
result_2 = await sessions["fda-drug-server"].call_tool("get_indications_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 OpenTargets drug associations
result_3 = await sessions["opentargets-server"].call_tool("get_associated_drugs_by_target_name", arguments={})
data_3 = parse(result_3)
print(f"Step 3 result: {json.dumps(data_3, indent=2, ensure_ascii=False)[:500]}")
# Step 4: Search PubMed for evidence
result_4 = await sessions["search-server"].call_tool("pubmed_search", 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())
Signals
- GitHub stars
- 167
- Forks
- 9
- Last commit
- Jun 2026
Advanced
- Catalog kind
- skill
- Gateway key
drug-indication-mapping- Source
- github.com/internscience/scp