Skill v1.0.1

currentAutomated scan100/100

internscience/scp/orphan-drug-analysis

1 files

──Details

PublishedJune 12, 2026 at 11:32 PM

Content Hashsha256:b1d22e1985b8b698...

Git SHAcea539856403

Bump Typepatch

Compare with v1.0.0

──Files

Files (1 file, 4.7 KB)

SKILL.md4.7 KBactive

SKILL.md · 119 lines · 4.7 KB

version: "1.0.1" name: orphan_drug_analysis description: "Orphan Drug & Rare Disease Analysis - Analyze orphan drugs: Monarch disease phenotypes, OpenTargets targets, FDA drug data, and clinical studies. Use this skill for orphan drug development tasks involving get joint associated diseases by HPO ID list get associated targets by disease efoId get clinical studies info by drug name pubmed search. Combines 4 tools from 4 SCP server(s)."

Orphan Drug & Rare Disease Analysis

Discipline: Orphan Drug Development | Tools Used: 4 | Servers: 4

Description

Analyze orphan drugs: Monarch disease phenotypes, OpenTargets targets, FDA drug data, and clinical studies.

Tools Used

`get_joint_associated_diseases_by_HPO_ID_list` from monarch-server (streamable-http) - https://scp.intern-ai.org.cn/api/v1/mcp/16/Origene-Monarch
`get_associated_targets_by_disease_efoId` from opentargets-server (streamable-http) - https://scp.intern-ai.org.cn/api/v1/mcp/15/Origene-OpenTargets
`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
`pubmed_search` from search-server (streamable-http) - https://scp.intern-ai.org.cn/api/v1/mcp/7/Origene-Search

Workflow

Map phenotypes to diseases
Find drug targets
Get clinical studies
Search literature

Test Case

Input

json

{
    "hpo_ids": [
        "HP:0001250"
    ],
    "disease_efo": "MONDO_0010075",
    "query": "orphan drug seizure disorder"
}

Expected Steps

Map phenotypes to diseases
Find drug targets
Get clinical studies
Search literature

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 = {
    "monarch-server": "https://scp.intern-ai.org.cn/api/v1/mcp/16/Origene-Monarch",
    "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",
    "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["monarch-server"], _, _ = await connect("https://scp.intern-ai.org.cn/api/v1/mcp/16/Origene-Monarch", "streamable-http")
    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")
    sessions["search-server"], _, _ = await connect("https://scp.intern-ai.org.cn/api/v1/mcp/7/Origene-Search", "streamable-http")
    # Execute workflow steps
    # Step 1: Map phenotypes to diseases
    result_1 = await sessions["monarch-server"].call_tool("get_joint_associated_diseases_by_HPO_ID_list", arguments={})
    data_1 = parse(result_1)
    print(f"Step 1 result: {json.dumps(data_1, indent=2, ensure_ascii=False)[:500]}")
    # Step 2: Find drug targets
    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: Get clinical studies
    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]}")
    # Step 4: Search literature
    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())

← v1.0.0 All versions