Data Analysis
Lee un dataset CSV/JSON y divide en train/validation/test con muestreo estratificado por la columna objetivo (70/15/15 por defecto, semilla reproducible), o valida con k-fold estratificado; incluye informe de distribución de clases por split con barras de desviación, comprobación de fugas por filas duplicadas, vista previa de SMOTE (interpolación de vecinos sobre el split de train) y exportación de los CSV en ZIP.
Llama a esta herramienta desde tu código en tres lenguajes.
# 1) Request a presigned URL → returns { uploadUrl, storageKey }
curl -X POST 'http://127.0.0.1:3003/api/upload/presign/train-test-split-with-stratification' \
-H 'Content-Type: application/json' \
-d '{"filename":"dataFile.ext","contentType":"application/octet-stream","size":12345}'
# 2) PUT the file bytes directly to the presigned uploadUrl
curl -X PUT '<presigned uploadUrl>' \
--data-binary @/path/to/file.ext
# 3) Call the tool, passing the returned storageKey for each file field
curl -X POST 'http://127.0.0.1:3003/es/api/tools/train-test-split-with-stratification' \
-F 'dataFile=uploads/2026/01/01/your-tool-1700000000000-abc123.ext' \
-F 'datasetText=text,length,label
The movie was wonderful and I loved every minute,42,positive
Absolutely terrible waste of time,31,negative
Pretty good overall would recommend,31,positive
Bad acting and a worse plot,25,negative
I enjoyed it a lot,16,positive
Not great not terrible just fine,30,neutral
Loved the soundtrack and visuals,28,positive
The pacing was boring and slow,28,negative
It was okay nothing special,24,neutral
Fantastic acting by the whole cast,30,positive
Weak ending ruined it for me,24,negative
Fine for a rainy afternoon,24,neutral
Brilliant and moving film,23,positive
Disappointing sequel,19,negative
Mediocre but watchable,21,neutral
Gorgeous cinematography throughout,29,positive
Painfully dull script,19,negative
A masterpiece of the genre,24,positive
Cliched and forgettable,20,negative
Charming and genuinely funny,25,positive
Skip it you will thank me,21,negative
Delightful from start to finish,27,positive
Two hours I will never get back,29,negative
Watchable if nothing else is on,26,neutral' \
-F 'targetColumn=label' \
-F 'splitMode=standard' \
-F 'trainPercent=70' \
-F 'valPercent=15' \
-F 'kFolds=5' \
-F 'randomSeed=42' \
-F 'stratify=true' \
-F 'shuffle=true' \
-F 'smotePreview=false' \
-F 'exportZip=true'Envía una petición POST con tus entradas en JSON. Los parámetros de tipo archivo requieren una subida previa.
POST http://127.0.0.1:3003/es/api/tools/train-test-split-with-stratification| Nombre | Tipo | Obligatorio | Descripción |
|---|---|---|---|
| dataFile | filesubida requerida | No | — |
| datasetText | textarea | No | — |
| targetColumn | text | Sí | — |
| splitMode | select | No | — |
| trainPercent | number | No | — |
| valPercent | number | No | — |
| kFolds | number | No | — |
| randomSeed | number | No | — |
| stratify | checkbox | No | — |
| shuffle | checkbox | No | — |
| smotePreview | checkbox | No | — |
| exportZip | checkbox | No | — |
Resultado HTML
{
"result": "<div>Processed HTML content</div>",
"error": "Error message (optional)",
"message": "Notification message (optional)",
"metadata": {
"key": "value"
}
}Añade esta herramienta a tu servidor Model Context Protocol para que los agentes de IA puedan listarla y llamarla.
Añade este bloque a la configuración de tu cliente MCP:
{
"mcpServers": {
"elysiatools-train-test-split-with-stratification": {
"name": "train-test-split-with-stratification",
"description": "Lee un dataset CSV/JSON y divide en train/validation/test con muestreo estratificado por la columna objetivo (70/15/15 por defecto, semilla reproducible), o valida con k-fold estratificado; incluye informe de distribución de clases por split con barras de desviación, comprobación de fugas por filas duplicadas, vista previa de SMOTE (interpolación de vecinos sobre el split de train) y exportación de los CSV en ZIP.",
"baseUrl": "http://127.0.0.1:3003/mcp/sse?toolId=train-test-split-with-stratification",
"command": "",
"args": [],
"env": {},
"isActive": true,
"type": "sse"
}
}
}Tras conectar al endpoint SSE, lista las herramientas expuestas:
{
"jsonrpc": "2.0",
"id": 1,
"method": "tools/list"
}Invoca la herramienta por su id; los argumentos se construyen a partir de sus parámetros:
{
"jsonrpc": "2.0",
"id": 2,
"method": "tools/call",
"params": {
"name": "train-test-split-with-stratification",
"arguments": {
"dataFile": "https://example.com/file.ext",
"datasetText": "text,length,label\nThe movie was wonderful and I loved every minute,42,positive\nAbsolutely terrible waste of time,31,negative\nPretty good overall would recommend,31,positive\nBad acting and a worse plot,25,negative\nI enjoyed it a lot,16,positive\nNot great not terrible just fine,30,neutral\nLoved the soundtrack and visuals,28,positive\nThe pacing was boring and slow,28,negative\nIt was okay nothing special,24,neutral\nFantastic acting by the whole cast,30,positive\nWeak ending ruined it for me,24,negative\nFine for a rainy afternoon,24,neutral\nBrilliant and moving film,23,positive\nDisappointing sequel,19,negative\nMediocre but watchable,21,neutral\nGorgeous cinematography throughout,29,positive\nPainfully dull script,19,negative\nA masterpiece of the genre,24,positive\nCliched and forgettable,20,negative\nCharming and genuinely funny,25,positive\nSkip it you will thank me,21,negative\nDelightful from start to finish,27,positive\nTwo hours I will never get back,29,negative\nWatchable if nothing else is on,26,neutral",
"targetColumn": "label",
"splitMode": "standard",
"trainPercent": 70,
"valPercent": 15,
"kFolds": 5,
"randomSeed": 42,
"stratify": true,
"shuffle": true,
"smotePreview": false,
"exportZip": true
}
}
}¿Dudas o problemas? Contacta con [email protected]