Science & Education
ΔT_b = K_b·m·i from a solvent preset table with van't Hoff factor support and custom solvents.
Call this tool from your code in three languages.
curl -X POST 'http://127.0.0.1:3003/en/api/tools/boiling-point-elevation' \
-H 'Content-Type: application/json' \
-d '{"solvent":"water","customKb":0,"customBoilingPoint":0,"molality":0.5,"iFactor":2}'Send a POST request with your inputs as JSON. File parameters require a separate upload first.
POST http://127.0.0.1:3003/en/api/tools/boiling-point-elevation| Name | Type | Required | Description |
|---|---|---|---|
| solvent | select | Yes | — |
| customKb | number | No | — |
| customBoilingPoint | number | No | — |
| molality | number | Yes | — |
| iFactor | number | Yes | — |
Add this tool to your Model Context Protocol server so AI agents can list and call it.
Add this block to your MCP client configuration:
{
"mcpServers": {
"elysiatools-boiling-point-elevation": {
"name": "boiling-point-elevation",
"description": "ΔT_b = K_b·m·i from a solvent preset table with van't Hoff factor support and custom solvents.",
"baseUrl": "http://127.0.0.1:3003/mcp/sse?toolId=boiling-point-elevation",
"command": "",
"args": [],
"env": {},
"isActive": true,
"type": "sse"
}
}
}After connecting to the SSE endpoint, list the exposed tools:
{
"jsonrpc": "2.0",
"id": 1,
"method": "tools/list"
}Invoke the tool by its id, passing arguments built from its parameters:
{
"jsonrpc": "2.0",
"id": 2,
"method": "tools/call",
"params": {
"name": "boiling-point-elevation",
"arguments": {
"solvent": "water",
"customKb": 0,
"customBoilingPoint": 0,
"molality": 0.5,
"iFactor": 2
}
}
}Questions or issues? Contact [email protected]
Text result
{
"result": "Processed text content",
"error": "Error message (optional)",
"message": "Notification message (optional)",
"metadata": {
"key": "value"
}
}