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Thermal & Heat Transfer Analysis - Analyze thermal system: calculate heat released, convert energy units, compute potential energy, and dynamic viscosity. Use this skill for thermal engineering tasks involving calculate heat released convert energy MeV to J calculate potential energy calculate dy...
---
name: thermal-analysis-0237c9dd71f5
description: "Thermal & Heat Transfer Analysis - Analyze thermal system: calculate heat released, convert energy units, compute potential energy, and dynamic viscosity. Use this skill for thermal engineering tasks involving calculate heat released convert energy MeV to J calculate potential energy calculate dynamic viscosity. Combines 4 tools from 1 SCP server(s)."
---
# Thermal & Heat Transfer Analysis
**Discipline**: Thermal Engineering | **Tools Used**: 4 | **Servers**: 1
## Description
Analyze thermal system: calculate heat released, convert energy units, compute potential energy, and dynamic viscosity.
## Tools Used
- **`calculate_heat_released`** from `server-22` (streamable-http) - `https://scp.intern-ai.org.cn/api/v1/mcp/22/Thermal_Fluid_Dynamics`
- **`convert_energy_MeV_to_J`** from `server-22` (streamable-http) - `https://scp.intern-ai.org.cn/api/v1/mcp/22/Thermal_Fluid_Dynamics`
- **`calculate_potential_energy`** from `server-22` (streamable-http) - `https://scp.intern-ai.org.cn/api/v1/mcp/22/Thermal_Fluid_Dynamics`
- **`calculate_dynamic_viscosity`** from `server-22` (streamable-http) - `https://scp.intern-ai.org.cn/api/v1/mcp/22/Thermal_Fluid_Dynamics`
## Workflow
1. Calculate heat released
2. Convert energy MeV to Joules
3. Calculate potential energy
4. Compute dynamic viscosity
## Test Case
### Input
```json
{
"energy_MeV": 5.0,
"mass": 10.0,
"height": 5.0
}
```
### Expected Steps
1. Calculate heat released
2. Convert energy MeV to Joules
3. Calculate potential energy
4. Compute dynamic viscosity
## 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 = {
"server-22": "https://scp.intern-ai.org.cn/api/v1/mcp/22/Thermal_Fluid_Dynamics"
}
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-22"] = await connect("https://scp.intern-ai.org.cn/api/v1/mcp/22/Thermal_Fluid_Dynamics", stack)
# Execute workflow steps
# Step 1: Calculate heat released
result_1 = await sessions["server-22"].call_tool("calculate_heat_released", arguments={})
data_1 = parse(result_1)
print(f"Step 1 result: {json.dumps(data_1, indent=2, ensure_ascii=False)[:500]}")
# Step 2: Convert energy MeV to Joules
result_2 = await sessions["server-22"].call_tool("convert_energy_MeV_to_J", arguments={})
data_2 = parse(result_2)
print(f"Step 2 result: {json.dumps(data_2, indent=2, ensure_ascii=False)[:500]}")
# Step 3: Calculate potential energy
result_3 = await sessions["server-22"].call_tool("calculate_potential_energy", arguments={})
data_3 = parse(result_3)
print(f"Step 3 result: {json.dumps(data_3, indent=2, ensure_ascii=False)[:500]}")
# Step 4: Compute dynamic viscosity
result_4 = await sessions["server-22"].call_tool("calculate_dynamic_viscosity", 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())
```
---
name: thermal-analysis-0237c9dd71f5
description: "Thermal & Heat Transfer Analysis - Analyze thermal system: calculate heat released, convert energy units, compute potential energy, and dynamic viscosity. Use this skill for thermal engineering tasks involving calculate heat released convert energy MeV to J calculate potential energy calculate dynamic viscosity. Combines 4 tools from 1 SCP server(s)."
---
# Thermal & Heat Transfer Analysis
**Discipline**: Thermal Engineering | **Tools Used**: 4 | **Servers**: 1
## Description
Analyze thermal system: calculate heat released, convert energy units, compute potential energy, and dynamic viscosity.
## Tools Used
- **`calculate_heat_released`** from `server-22` (streamable-http) - `https://scp.intern-ai.org.cn/api/v1/mcp/22/Thermal_Fluid_Dynamics`
- **`convert_energy_MeV_to_J`** from `server-22` (streamable-http) - `https://scp.intern-ai.org.cn/api/v1/mcp/22/Thermal_Fluid_Dynamics`
- **`calculate_potential_energy`** from `server-22` (streamable-http) - `https://scp.intern-ai.org.cn/api/v1/mcp/22/Thermal_Fluid_Dynamics`
- **`calculate_dynamic_viscosity`** from `server-22` (streamable-http) - `https://scp.intern-ai.org.cn/api/v1/mcp/22/Thermal_Fluid_Dynamics`
## Workflow
1. Calculate heat released
2. Convert energy MeV to Joules
3. Calculate potential energy
4. Compute dynamic viscosity
## Test Case
### Input
```json
{
"energy_MeV": 5.0,
"mass": 10.0,
"height": 5.0
}
```
### Expected Steps
1. Calculate heat released
2. Convert energy MeV to Joules
3. Calculate potential energy
4. Compute dynamic viscosity
## 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 = {
"server-22": "https://scp.intern-ai.org.cn/api/v1/mcp/22/Thermal_Fluid_Dynamics"
}
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-22"] = await connect("https://scp.intern-ai.org.cn/api/v1/mcp/22/Thermal_Fluid_Dynamics", stack)
# Execute workflow steps
# Step 1: Calculate heat released
result_1 = await sessions["server-22"].call_tool("calculate_heat_released", arguments={})
data_1 = parse(result_1)
print(f"Step 1 result: {json.dumps(data_1, indent=2, ensure_ascii=False)[:500]}")
# Step 2: Convert energy MeV to Joules
result_2 = await sessions["server-22"].call_tool("convert_energy_MeV_to_J", arguments={})
data_2 = parse(result_2)
print(f"Step 2 result: {json.dumps(data_2, indent=2, ensure_ascii=False)[:500]}")
# Step 3: Calculate potential energy
result_3 = await sessions["server-22"].call_tool("calculate_potential_energy", arguments={})
data_3 = parse(result_3)
print(f"Step 3 result: {json.dumps(data_3, indent=2, ensure_ascii=False)[:500]}")
# Step 4: Compute dynamic viscosity
result_4 = await sessions["server-22"].call_tool("calculate_dynamic_viscosity", 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())
```
Imported from the listed AgentSkill.sh category. Complete supplied package files and notices are retained. Required external tools and credentials are not installed. No bundled code was executed during import. Individual agent task behavior has not been tested.