cell_line_assay_analysis: Скилл анализа клеточных тестов

--- 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 ```json { "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 `sk-b04409a1-b32b-4511-9aeb-22980abdc05c` with your own SCP Hub API Key. You can obtain one from the [SCP Platform](https://scphub.intern-ai.org.cn). ```python 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 = { "chembl-server": "https://scp.intern-ai.org.cn/api/v1/mcp/4/Origene-ChEMBL" } 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["chembl-server"] = await connect("https://scp.intern-ai.org.cn/api/v1/mcp/4/Origene-ChEMBL", stack) # 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()) ```