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PublishedJune 17, 2026 at 10:41 AM

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version: "1.0.0" name: disease_drug_landscape description: "Disease-Drug Landscape Analysis - Map the drug landscape for a disease: OpenTargets disease drugs, FDA indications, and clinical studies. Use this skill for drug discovery tasks involving get associated drugs by target name get drug names by indication get clinical studies info by drug name. Combines 3 tools from 2 SCP server(s)."

Disease-Drug Landscape Analysis

Discipline: Drug Discovery | Tools Used: 3 | Servers: 2

Description

Map the drug landscape for a disease: OpenTargets disease drugs, FDA indications, and clinical studies.

Tools Used

`get_associated_drugs_by_target_name` from opentargets-server (streamable-http) - https://scp.intern-ai.org.cn/api/v1/mcp/15/Origene-OpenTargets
`get_drug_names_by_indication` from fda-drug-server (streamable-http) - https://scp.intern-ai.org.cn/api/v1/mcp/14/Origene-FDADrug
`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

Workflow

Get associated drugs from OpenTargets
Find drugs by indication in FDA
Get clinical studies for top drug

Test Case

Input

json

{
    "target_name": "EGFR",
    "indication": "non-small cell lung cancer"
}

Expected Steps

Get associated drugs from OpenTargets
Find drugs by indication in FDA
Get clinical studies for top drug

Usage Example

Note: Replace <YOUR_SCP_HUB_API_KEY> with your own SCP Hub API Key. You can obtain one from the SCP Platform.

python

import asyncio
import json
from mcp import ClientSession
from mcp.client.streamable_http import streamablehttp_client
from mcp.client.sse import sse_client
SERVERS = {
    "opentargets-server": "https://scp.intern-ai.org.cn/api/v1/mcp/15/Origene-OpenTargets",
    "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["opentargets-server"], _, _ = await connect("https://scp.intern-ai.org.cn/api/v1/mcp/15/Origene-OpenTargets", "streamable-http")
    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 associated drugs from OpenTargets
    result_1 = await sessions["opentargets-server"].call_tool("get_associated_drugs_by_target_name", arguments={})
    data_1 = parse(result_1)
    print(f"Step 1 result: {json.dumps(data_1, indent=2, ensure_ascii=False)[:500]}")
    # Step 2: Find drugs by indication in FDA
    result_2 = await sessions["fda-drug-server"].call_tool("get_drug_names_by_indication", arguments={})
    data_2 = parse(result_2)
    print(f"Step 2 result: {json.dumps(data_2, indent=2, ensure_ascii=False)[:500]}")
    # Step 3: Get clinical studies for top drug
    result_3 = await sessions["fda-drug-server"].call_tool("get_clinical_studies_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]}")
    # Cleanup
    print("Workflow complete!")
if __name__ == "__main__":
    asyncio.run(main())

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