code-execution-analysis

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

{
    "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 sk-b04409a1-b32b-4511-9aeb-22980abdc05c with your own SCP Hub API Key. You can obtain one from the SCP Platform.

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 = {
    "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, 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["server-18"] = await connect("https://scp.intern-ai.org.cn/api/v1/mcp/18/Thoth-OP", stack)
        sessions["server-1"] = await connect("https://scp.intern-ai.org.cn/api/v1/mcp/1/VenusFactory", stack)

        # 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())