Science & Education
φ = artanh(β) with γ = cosh φ, γβ = sinh φ, the e^φ Doppler factor, and the tanh(2φ) additivity check — where boosts just add.
Call this tool from your code in three languages.
curl -X POST 'http://127.0.0.1:3003/en/api/tools/rapidity-calculator' \
-H 'Content-Type: application/json' \
-d '{"speedMode":"beta","beta":0.5,"speedMs":0}'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/rapidity-calculator| Name | Type | Required | Description |
|---|---|---|---|
| speedMode | select | Yes | — |
| beta | number | No | — |
| speedMs | number | No | — |
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-rapidity-calculator": {
"name": "rapidity-calculator",
"description": "φ = artanh(β) with γ = cosh φ, γβ = sinh φ, the e^φ Doppler factor, and the tanh(2φ) additivity check — where boosts just add.",
"baseUrl": "http://127.0.0.1:3003/mcp/sse?toolId=rapidity-calculator",
"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": "rapidity-calculator",
"arguments": {
"speedMode": "beta",
"beta": 0.5,
"speedMs": 0
}
}
}Questions or issues? Contact [email protected]