experimental-data-processing

name: experimental_data_processing description: "Experimental Data Processing - Process experimental data: absolute error, mean square, max value, scientific notation formatting. Use this skill for experimental physics tasks involving calculate absolute error calculate mean square calculate max value format scientific notation convert to scientific notation. Combines 5 tools from 1 SCP server(s)."

Experimental Data Processing

Discipline: Experimental Physics | Tools Used: 5 | Servers: 1

Description

Process experimental data: absolute error, mean square, max value, scientific notation formatting.

Tools Used

• calculate_absolute_error from server-26 (streamable-http) - https://scp.intern-ai.org.cn/api/v1/mcp/26/Data_processing_and_statistical_analysis
• calculate_mean_square from server-26 (streamable-http) - https://scp.intern-ai.org.cn/api/v1/mcp/26/Data_processing_and_statistical_analysis
• calculate_max_value from server-26 (streamable-http) - https://scp.intern-ai.org.cn/api/v1/mcp/26/Data_processing_and_statistical_analysis
• format_scientific_notation from server-26 (streamable-http) - https://scp.intern-ai.org.cn/api/v1/mcp/26/Data_processing_and_statistical_analysis
• convert_to_scientific_notation from server-26 (streamable-http) - https://scp.intern-ai.org.cn/api/v1/mcp/26/Data_processing_and_statistical_analysis

Workflow

1. Calculate absolute errors
2. Compute mean square
3. Find maximum
4. Format in scientific notation
5. Summarize results

Test Case

Input

{
    "measurements": [
        9.78,
        9.81,
        9.83,
        9.79,
        9.8
    ],
    "true_value": 9.81
}

Expected Steps

1. Calculate absolute errors
2. Compute mean square
3. Find maximum
4. Format in scientific notation
5. Summarize results

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-26": "https://scp.intern-ai.org.cn/api/v1/mcp/26/Data_processing_and_statistical_analysis"
}

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-26"] = await connect("https://scp.intern-ai.org.cn/api/v1/mcp/26/Data_processing_and_statistical_analysis", stack)

        # Execute workflow steps
        # Step 1: Calculate absolute errors
        result_1 = await sessions["server-26"].call_tool("calculate_absolute_error", arguments={})
        data_1 = parse(result_1)
        print(f"Step 1 result: {json.dumps(data_1, indent=2, ensure_ascii=False)[:500]}")

        # Step 2: Compute mean square
        result_2 = await sessions["server-26"].call_tool("calculate_mean_square", arguments={})
        data_2 = parse(result_2)
        print(f"Step 2 result: {json.dumps(data_2, indent=2, ensure_ascii=False)[:500]}")

        # Step 3: Find maximum
        result_3 = await sessions["server-26"].call_tool("calculate_max_value", arguments={})
        data_3 = parse(result_3)
        print(f"Step 3 result: {json.dumps(data_3, indent=2, ensure_ascii=False)[:500]}")

        # Step 4: Format in scientific notation
        result_4 = await sessions["server-26"].call_tool("format_scientific_notation", arguments={})
        data_4 = parse(result_4)
        print(f"Step 4 result: {json.dumps(data_4, indent=2, ensure_ascii=False)[:500]}")

        # Step 5: Summarize results
        result_5 = await sessions["server-26"].call_tool("convert_to_scientific_notation", arguments={})
        data_5 = parse(result_5)
        print(f"Step 5 result: {json.dumps(data_5, indent=2, ensure_ascii=False)[:500]}")

        # Cleanup
        print("Workflow complete!")

if __name__ == "__main__":
    asyncio.run(main())