Science & Education
Initial-rate method: single-reactant log-log fit or two-reactant method of pairs, with k back-calculated per experiment.
Call this tool from your code in three languages.
curl -X POST 'http://127.0.0.1:3003/en/api/tools/reaction-order-determiner' \
-H 'Content-Type: application/json' \
-d '{"experiments":"0.10 0.02\n0.20 0.08\n0.40 0.32"}'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-order-determiner| Name | Type | Required | Description |
|---|---|---|---|
| experiments | textarea | Yes | — |
Text result
{
"result": "Processed text content",
"error": "Error message (optional)",
"message": "Notification message (optional)",
"metadata": {
"key": "value"
}
}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-order-determiner": {
"name": "reaction-order-determiner",
"description": "Initial-rate method: single-reactant log-log fit or two-reactant method of pairs, with k back-calculated per experiment.",
"baseUrl": "http://127.0.0.1:3003/mcp/sse?toolId=reaction-order-determiner",
"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-order-determiner",
"arguments": {
"experiments": "0.10 0.02\n0.20 0.08\n0.40 0.32"
}
}
}Questions or issues? Contact [email protected]