energy_conversion
$
npx mdskill add InternScience/scp/energy_conversionConverts energy units and analyzes results using physics-specific tools
- Solves physics tasks requiring MeV to Joules conversion and error analysis
- Uses 4 tools from servers 22 and 26 for unit conversion and formatting
- Processes input through a pipeline of conversion, notation formatting, and error calculation
- Delivers results in scientific notation with absolute error values
SKILL.md
.github/skills/energy_conversionView on GitHub ↗
---
name: energy_conversion
description: "Energy Unit Conversion Pipeline - Convert between energy units and analyze: MeV to Joules, scientific notation, and error calculation. Use this skill for physics tasks involving convert energy MeV to J convert to scientific notation format scientific notation calculate absolute error. Combines 4 tools from 2 SCP server(s)."
---
# Energy Unit Conversion Pipeline
**Discipline**: Physics | **Tools Used**: 4 | **Servers**: 2
## Description
Convert between energy units and analyze: MeV to Joules, scientific notation, and error calculation.
## Tools Used
- **`convert_energy_MeV_to_J`** from `server-22` (streamable-http) - `https://scp.intern-ai.org.cn/api/v1/mcp/22/Thermal_Fluid_Dynamics`
- **`convert_to_scientific_notation`** 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`
- **`calculate_absolute_error`** from `server-26` (streamable-http) - `https://scp.intern-ai.org.cn/api/v1/mcp/26/Data_processing_and_statistical_analysis`
## Workflow
1. Convert MeV to Joules
2. Convert to scientific notation
3. Format result
4. Calculate conversion error
## Test Case
### Input
```json
{
"energy_MeV": 13.6,
"expected_J": 2.18e-12
}
```
### Expected Steps
1. Convert MeV to Joules
2. Convert to scientific notation
3. Format result
4. Calculate conversion error
## 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-22": "https://scp.intern-ai.org.cn/api/v1/mcp/22/Thermal_Fluid_Dynamics",
"server-26": "https://scp.intern-ai.org.cn/api/v1/mcp/26/Data_processing_and_statistical_analysis"
}
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-22"], _, _ = await connect("https://scp.intern-ai.org.cn/api/v1/mcp/22/Thermal_Fluid_Dynamics", "streamable-http")
sessions["server-26"], _, _ = await connect("https://scp.intern-ai.org.cn/api/v1/mcp/26/Data_processing_and_statistical_analysis", "streamable-http")
# Execute workflow steps
# Step 1: Convert MeV to Joules
result_1 = await sessions["server-22"].call_tool("convert_energy_MeV_to_J", arguments={})
data_1 = parse(result_1)
print(f"Step 1 result: {json.dumps(data_1, indent=2, ensure_ascii=False)[:500]}")
# Step 2: Convert to scientific notation
result_2 = await sessions["server-26"].call_tool("convert_to_scientific_notation", arguments={})
data_2 = parse(result_2)
print(f"Step 2 result: {json.dumps(data_2, indent=2, ensure_ascii=False)[:500]}")
# Step 3: Format result
result_3 = await sessions["server-26"].call_tool("format_scientific_notation", arguments={})
data_3 = parse(result_3)
print(f"Step 3 result: {json.dumps(data_3, indent=2, ensure_ascii=False)[:500]}")
# Step 4: Calculate conversion error
result_4 = await sessions["server-26"].call_tool("calculate_absolute_error", 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())
```