Drug-Drug Interaction Checker

SkillDev tools

Lets your agent check interactions between multiple drugs using FDA and PubChem data.

Use Drug-Drug Interaction Checker in Claude, ChatGPT or Ahel Desktop

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Also: Claude Code · Cursor · Codex

Then ask your AI: use the Drug-Drug Interaction Checker skill

Details

Instructions available. Your AI can read the instructions. Execution depends on the setup they require.

Add Ahel to your AI once: Claude, ChatGPT, Cursor, Claude Code or Codex. Then ask it to use this.

Drug-Drug Interaction CheckerStart free
About this skill

The largest open-source medical AI skills library for OpenClaw🦞.

What this skill tells your AI

The instructions your AI receives, as published by freedomintelligence/openclaw-medical-skills in skills/drug-interaction-checker/SKILL.md and read by Ahel’s review.

Discipline: Clinical Pharmacology | Tools Used: 3 | Servers: 3

Description

Check interactions between multiple drugs using FDA interaction data, PubChem compound info, and ChEMBL target overlap analysis.

Tools Used

  • get_drug_interactions_by_drug_name from fda-drug-server (streamable-http) - https://scp.intern-ai.org.cn/api/v1/mcp/14/Origene-FDADrug
  • get_compound_by_name from pubchem-server (streamable-http) - https://scp.intern-ai.org.cn/api/v1/mcp/8/Origene-PubChem
  • get_target_by_name from chembl-server (streamable-http) - https://scp.intern-ai.org.cn/api/v1/mcp/4/Origene-ChEMBL

Workflow

  1. Get interactions for drug A
  2. Get interactions for drug B
  3. Compare compound targets from ChEMBL

Test Case

Input

{
    "drug_a": "warfarin",
    "drug_b": "aspirin"
}

Expected Steps

  1. Get interactions for drug A
  2. Get interactions for drug B
  3. Compare compound targets from ChEMBL

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 = {
    "fda-drug-server": "https://scp.intern-ai.org.cn/api/v1/mcp/14/Origene-FDADrug",
    "pubchem-server": "https://scp.intern-ai.org.cn/api/v1/mcp/8/Origene-PubChem",
    "chembl-server": "https://scp.intern-ai.org.cn/api/v1/mcp/4/Origene-ChEMBL"
}

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["fda-drug-server"], _, _ = await connect("https://scp.intern-ai.org.cn/api/v1/mcp/14/Origene-FDADrug", "streamable-http")
    sessions["pubchem-server"], _, _ = await connect("https://scp.intern-ai.org.cn/api/v1/mcp/8/Origene-PubChem", "streamable-http")
    sessions["chembl-server"], _, _ = await connect("https://scp.intern-ai.org.cn/api/v1/mcp/4/Origene-ChEMBL", "streamable-http")

    # Execute workflow steps
    # Step 1: Get interactions for drug A
    result_1 = await sessions["fda-drug-server"].call_tool("get_drug_interactions_by_drug_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 interactions for drug B
    result_2 = await sessions["pubchem-server"].call_tool("get_compound_by_name", arguments={})
    data_2 = parse(result_2)
    print(f"Step 2 result: {json.dumps(data_2, indent=2, ensure_ascii=False)[:500]}")

    # Step 3: Compare compound targets from ChEMBL
    result_3 = await sessions["chembl-server"].call_tool("get_target_by_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())

Signals

GitHub stars
3k
Forks
412
Last commit
Jul 2026
Advanced
Item type
skill
Key
drug-interaction-checker
Source
github.com/freedomintelligence/openclaw-medical-skills