Development
Map messy CSV headers onto a clean target schema. Paste a CSV whose first row holds inconsistent column names (Customer ID, user_id, customerId, CUST_ID…) plus your target schema (one column per line) and an optional "alias=target" dictionary. Each source header is resolved through four layered passes — exact, dictionary, normalized (strips case/accents/separators/camelCase), then fuzzy edit-distance — and shown in a mapping table with method and confidence, plus target coverage and an optional normalized CSV export.
Call this tool from your code in three languages.
curl -X POST 'https://api.elysiatools.com/en/api/tools/csv-header-alias-resolver' \
-H 'Content-Type: application/json' \
-d '{"csvInput":"CUST_ID,First Name,e-mail,Phone Number\n1,Ada,[email protected],+1-555\n2,Linus,[email protected],+1-556","delimiter":",","targetSchema":"customerId\nfirstName\nemail\nphone","aliasDict":"cust id=customerId\ncustomer id=customerId\nphone number=phone","threshold":"0.8","outputCsv":true}'Send a POST request with your inputs as JSON. File parameters require a separate upload first.
POST https://api.elysiatools.com/en/api/tools/csv-header-alias-resolver| Name | Type | Required | Description |
|---|---|---|---|
| csvInput | textarea | Yes | — |
| delimiter | select | No | — |
| targetSchema | textarea | Yes | — |
| aliasDict | textarea | No | — |
| threshold | select | No | — |
| outputCsv |
| checkbox |
| No |
| — |
HTML result
{
"result": "<div>Processed HTML content</div>",
"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-csv-header-alias-resolver": {
"name": "csv-header-alias-resolver",
"description": "Map messy CSV headers onto a clean target schema. Paste a CSV whose first row holds inconsistent column names (Customer ID, user_id, customerId, CUST_ID…) plus your target schema (one column per line) and an optional \"alias=target\" dictionary. Each source header is resolved through four layered passes — exact, dictionary, normalized (strips case/accents/separators/camelCase), then fuzzy edit-distance — and shown in a mapping table with method and confidence, plus target coverage and an optional normalized CSV export.",
"baseUrl": "https://api.elysiatools.com/mcp/sse?toolId=csv-header-alias-resolver",
"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": "csv-header-alias-resolver",
"arguments": {
"csvInput": "CUST_ID,First Name,e-mail,Phone Number\n1,Ada,[email protected],+1-555\n2,Linus,[email protected],+1-556",
"delimiter": ",",
"targetSchema": "customerId\nfirstName\nemail\nphone",
"aliasDict": "cust id=customerId\ncustomer id=customerId\nphone number=phone",
"threshold": "0.8",
"outputCsv": true
}
}
}Questions or issues? Contact [email protected]