Gene-Variant-Drug Nexus

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Gene-Variant-Drug Nexus - Connect gene variants to drugs: variant effect, gene-disease link, drug associations, and clinical evidence. Use this skill for translational genomics tasks involving get vep hgvs get associated targets by disease efoId get associated drugs by target name clinvar search. Combines 4 tools from 3 SCP server(s).

Available today. Use it from your connected AI after setup.

Connect ahel once, and every AI you use reads what you have installed.

Then ask your AI: use the Gene-Variant-Drug Nexus skill

What this skill tells your AI

The instructions your AI receives, as published by spectrai-initiative/innoclaw in .claude/skills/gene_variant_drug_nexus/SKILL.md and read by ahel’s review.

Discipline: Translational Genomics | Tools Used: 4 | Servers: 3

Description

Connect gene variants to drugs: variant effect, gene-disease link, drug associations, and clinical evidence.

Tools Used

  • get_vep_hgvs from ensembl-server (streamable-http) - https://scp.intern-ai.org.cn/api/v1/mcp/12/Origene-Ensembl
  • get_associated_targets_by_disease_efoId from opentargets-server (streamable-http) - https://scp.intern-ai.org.cn/api/v1/mcp/15/Origene-OpenTargets
  • get_associated_drugs_by_target_name from opentargets-server (streamable-http) - https://scp.intern-ai.org.cn/api/v1/mcp/15/Origene-OpenTargets
  • clinvar_search from search-server (streamable-http) - https://scp.intern-ai.org.cn/api/v1/mcp/7/Origene-Search

Workflow

  1. Predict variant effect
  2. Get disease-target associations
  3. Find drugs for target
  4. Check ClinVar clinical significance

Test Case

Input

{
    "hgvs": "ENSP00000269305.4:p.Arg175His",
    "disease_efo": "EFO_0000311"
}

Expected Steps

  1. Predict variant effect
  2. Get disease-target associations
  3. Find drugs for target
  4. Check ClinVar clinical significance

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 = {
    "ensembl-server": "https://scp.intern-ai.org.cn/api/v1/mcp/12/Origene-Ensembl",
    "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["ensembl-server"] = await connect("https://scp.intern-ai.org.cn/api/v1/mcp/12/Origene-Ensembl", 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: Predict variant effect
        result_1 = await sessions["ensembl-server"].call_tool("get_vep_hgvs", arguments={})
        data_1 = parse(result_1)
        print(f"Step 1 result: {json.dumps(data_1, indent=2, ensure_ascii=False)[:500]}")

        # Step 2: Get disease-target associations
        result_2 = await sessions["opentargets-server"].call_tool("get_associated_targets_by_disease_efoId", arguments={})
        data_2 = parse(result_2)
        print(f"Step 2 result: {json.dumps(data_2, indent=2, ensure_ascii=False)[:500]}")

        # Step 3: Find drugs for target
        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: Check ClinVar clinical significance
        result_4 = await sessions["search-server"].call_tool("clinvar_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
391
Forks
28
Last commit
Aug 2026
Advanced
Catalog kind
skill
Gateway key
gene-variant-drug-nexus-spectrai-initiative
Source
github.com/spectrai-initiative/innoclaw