Анализ кода: code_execution_analysis — Скилл для науки

--- name: code_execution_analysis description: "Computational Analysis via Code Execution - Execute custom computational analysis code, analyze software, and search for reference implementations. Use this skill for computational science tasks involving exec code software analysis search dataset search literature. Combines 4 tools from 2 SCP server(s)." --- # Computational Analysis via Code Execution **Discipline**: Computational Science | **Tools Used**: 4 | **Servers**: 2 ## Description Execute custom computational analysis code, analyze software, and search for reference implementations. ## Tools Used - **`exec_code`** from `server-18` (streamable-http) - `https://scp.intern-ai.org.cn/api/v1/mcp/18/Thoth-OP` - **`software_analysis`** from `server-18` (streamable-http) - `https://scp.intern-ai.org.cn/api/v1/mcp/18/Thoth-OP` - **`search_dataset`** from `server-1` (sse) - `https://scp.intern-ai.org.cn/api/v1/mcp/1/VenusFactory` - **`search_literature`** from `server-1` (sse) - `https://scp.intern-ai.org.cn/api/v1/mcp/1/VenusFactory` ## Workflow 1. Execute analysis code 2. Analyze software requirements 3. Search for datasets 4. Search for methods literature ## Test Case ### Input ```json { "code": "print('hello')", "query": "machine learning protein prediction" } ``` ### Expected Steps 1. Execute analysis code 2. Analyze software requirements 3. Search for datasets 4. Search for methods 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](https://scphub.intern-ai.org.cn). ```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 = { "server-18": "https://scp.intern-ai.org.cn/api/v1/mcp/18/Thoth-OP", "server-1": "https://scp.intern-ai.org.cn/api/v1/mcp/1/VenusFactory" } 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["server-18"], _, _ = await connect("https://scp.intern-ai.org.cn/api/v1/mcp/18/Thoth-OP", "streamable-http") sessions["server-1"], _, _ = await connect("https://scp.intern-ai.org.cn/api/v1/mcp/1/VenusFactory", "sse") # Execute workflow steps # Step 1: Execute analysis code result_1 = await sessions["server-18"].call_tool("exec_code", arguments={}) data_1 = parse(result_1) print(f"Step 1 result: {json.dumps(data_1, indent=2, ensure_ascii=False)[:500]}") # Step 2: Analyze software requirements result_2 = await sessions["server-18"].call_tool("software_analysis", arguments={}) data_2 = parse(result_2) print(f"Step 2 result: {json.dumps(data_2, indent=2, ensure_ascii=False)[:500]}") # Step 3: Search for datasets result_3 = await sessions["server-1"].call_tool("search_dataset", arguments={}) data_3 = parse(result_3) print(f"Step 3 result: {json.dumps(data_3, indent=2, ensure_ascii=False)[:500]}") # Step 4: Search for methods literature result_4 = await sessions["server-1"].call_tool("search_literature", 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()) ```