Cell Line Assay Analysis
SkillSearchCell 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).
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Then ask your AI: use the Cell Line Assay Analysis skill
What this skill tells your AI
The instructions your AI receives, as published by internscience/scp in skills/cell_line_assay_analysis/SKILL.md and read by ahel’s review.
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_idfromchembl-server(streamable-http) -https://scp.intern-ai.org.cn/api/v1/mcp/4/Origene-ChEMBLsearch_assayfromchembl-server(streamable-http) -https://scp.intern-ai.org.cn/api/v1/mcp/4/Origene-ChEMBLsearch_activityfromchembl-server(streamable-http) -https://scp.intern-ai.org.cn/api/v1/mcp/4/Origene-ChEMBLget_target_by_namefromchembl-server(streamable-http) -https://scp.intern-ai.org.cn/api/v1/mcp/4/Origene-ChEMBL
Workflow
- Get cell line info
- Search assays for cell line
- Search activity data
- Get target info
Test Case
Input
{
"cell_id": 1,
"assay_query": "MCF7",
"target": "estrogen receptor"
}
Expected Steps
- Get cell line info
- Search assays for cell line
- Search activity data
- 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())
Signals
- GitHub stars
- 167
- Forks
- 9
- Last commit
- Jun 2026
Advanced
- Catalog kind
- skill
- Gateway key
cell-line-assay-analysis- Source
- github.com/internscience/scp