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drug_indication_mapping

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Drug-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).

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Drug-Indication Mapping

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_id from chembl-server (streamable-http) - https://scp.intern-ai.org.cn/api/v1/mcp/4/Origene-ChEMBL
  • get_indications_by_drug_name from fda-drug-server (streamable-http) - https://scp.intern-ai.org.cn/api/v1/mcp/14/Origene-FDADrug
  • get_associated_drugs_by_target_name from opentargets-server (streamable-http) - https://scp.intern-ai.org.cn/api/v1/mcp/15/Origene-OpenTargets
  • pubmed_search from search-server (streamable-http) - https://scp.intern-ai.org.cn/api/v1/mcp/7/Origene-Search

Workflow

  1. Get ChEMBL indication data
  2. Get FDA indications
  3. Get OpenTargets drug associations
  4. Search PubMed for evidence

Test Case

Input

{
    "drug_name": "imatinib",
    "drugind_id": 1
}

Expected Steps

  1. Get ChEMBL indication data
  2. Get FDA indications
  3. Get OpenTargets drug associations
  4. Search PubMed for evidence

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 = {
    "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, 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["chembl-server"] = await connect("https://scp.intern-ai.org.cn/api/v1/mcp/4/Origene-ChEMBL", stack)
        sessions["fda-drug-server"] = await connect("https://scp.intern-ai.org.cn/api/v1/mcp/14/Origene-FDADrug", stack)
        sessions["opentargets-server"] = await connect("https://scp.intern-ai.org.cn/api/v1/mcp/15/Origene-OpenTargets", stack)
        sessions["search-server"] = await connect("https://scp.intern-ai.org.cn/api/v1/mcp/7/Origene-Search", stack)

        # 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())