Science & Education
k(T) from a reference point and Ea via the Arrhenius ratio, with kJ/kcal/J activation energy conversion built in.
Call this tool from your code in three languages.
curl -X POST 'http://127.0.0.1:3003/en/api/tools/reaction-rate-constant-estimator' \
-H 'Content-Type: application/json' \
-d '{"kRef":0.05,"tRef":298.15,"tTarget":323.15,"tUnit":"K","ea":60,"eaUnit":"kJ"}'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/reaction-rate-constant-estimator| Name | Type | Required | Description |
|---|---|---|---|
| kRef | number | Yes | — |
| tRef | number | Yes | — |
| tTarget | number | Yes | — |
| tUnit | select | Yes | — |
| ea | number | Yes | — |
| eaUnit |
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-reaction-rate-constant-estimator": {
"name": "reaction-rate-constant-estimator",
"description": "k(T) from a reference point and Ea via the Arrhenius ratio, with kJ/kcal/J activation energy conversion built in.",
"baseUrl": "http://127.0.0.1:3003/mcp/sse?toolId=reaction-rate-constant-estimator",
"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": "reaction-rate-constant-estimator",
"arguments": {
"kRef": 0.05,
"tRef": 298.15,
"tTarget": 323.15,
"tUnit": "K",
"ea": 60,
"eaUnit": "kJ"
}
}
}Questions or issues? Contact [email protected]
| select |
| Yes |
| — |
Text result
{
"result": "Processed text content",
"error": "Error message (optional)",
"message": "Notification message (optional)",
"metadata": {
"key": "value"
}
}