cell-line-assay-analysis

name: cell_line_assay_analysis description: "Cell Line Assay Analysis - Analyze cell line assays: ChEMBL cell line info, assay search, activity data, and target info. Use this skill for cell biology tasks involving get cell line by id search assay search activity get target by name. Combines 4 tools from 1 SCP server(s)."

Cell Line Assay Analysis

Discipline: Cell Biology | Tools Used: 4 | Servers: 1

Description

Analyze cell line assays: ChEMBL cell line info, assay search, activity data, and target info.

Tools Used

• get_cell_line_by_id from chembl-server (streamable-http) - https://scp.intern-ai.org.cn/api/v1/mcp/4/Origene-ChEMBL
• search_assay from chembl-server (streamable-http) - https://scp.intern-ai.org.cn/api/v1/mcp/4/Origene-ChEMBL
• search_activity from chembl-server (streamable-http) - https://scp.intern-ai.org.cn/api/v1/mcp/4/Origene-ChEMBL
• get_target_by_name from chembl-server (streamable-http) - https://scp.intern-ai.org.cn/api/v1/mcp/4/Origene-ChEMBL

Workflow

1. Get cell line info
2. Search assays for cell line
3. Search activity data
4. Get target info

Test Case

Input

{
    "cell_id": 1,
    "assay_query": "MCF7",
    "target": "estrogen receptor"
}

Expected Steps

1. Get cell line info
2. Search assays for cell line
3. Search activity data
4. Get target info

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 = {
    "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["chembl-server"], _, _ = await connect("https://scp.intern-ai.org.cn/api/v1/mcp/4/Origene-ChEMBL", "streamable-http")

    # Execute workflow steps
    # Step 1: Get cell line info
    result_1 = await sessions["chembl-server"].call_tool("get_cell_line_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: Search assays for cell line
    result_2 = await sessions["chembl-server"].call_tool("search_assay", arguments={})
    data_2 = parse(result_2)
    print(f"Step 2 result: {json.dumps(data_2, indent=2, ensure_ascii=False)[:500]}")

    # Step 3: Search activity data
    result_3 = await sessions["chembl-server"].call_tool("search_activity", arguments={})
    data_3 = parse(result_3)
    print(f"Step 3 result: {json.dumps(data_3, indent=2, ensure_ascii=False)[:500]}")

    # Step 4: Get target info
    result_4 = await sessions["chembl-server"].call_tool("get_target_by_name", 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())