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Data Processing

Concat Arrays

Concatenate multiple arrays using lodash _.concat

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Quick start

Call this tool from your code in three languages.

cURL
curl -X POST 'https://api.elysiatools.com/en/api/tools/concat-arrays' \
  -H 'Content-Type: application/json' \
  -d '{"array1":"[1, 2]","array2":"[3, 4]","array3":"[5, 6]","array4":"[7, 8]","array5":"[9, 10]","flattenMode":"shallow","unique":false}'

API reference

Send a POST request with your inputs as JSON. File parameters require a separate upload first.

Endpoint

HTTP
POST https://api.elysiatools.com/en/api/tools/concat-arrays

Parameters

NameTypeRequiredDescription
array1textareaYes—
array2textareaNo—
array3textareaNo—
array4textareaNo—
array5textareaNo—
flattenMode

MCP integration

Add this tool to your Model Context Protocol server so AI agents can list and call it.

Server configuration

Add this block to your MCP client configuration:

mcp.json
{
  "mcpServers": {
    "elysiatools-concat-arrays": {
      "name": "concat-arrays",
      "description": "Concatenate multiple arrays using lodash _.concat",
      "baseUrl": "https://api.elysiatools.com/mcp/sse?toolId=concat-arrays",
      "command": "",
      "args": [],
      "env": {},
      "isActive": true,
      "type": "sse"
    }
  }
}

List available tools

After connecting to the SSE endpoint, list the exposed tools:

tools/list
{
  "jsonrpc": "2.0",
  "id": 1,
  "method": "tools/list"
}

Call this tool

Invoke the tool by its id, passing arguments built from its parameters:

tools/call
{
  "jsonrpc": "2.0",
  "id": 2,
  "method": "tools/call",
  "params": {
    "name": "concat-arrays",
    "arguments": {
  "array1": "[1, 2]",
  "array2": "[3, 4]",
  "array3": "[5, 6]",
  "array4": "[7, 8]",
  "array5": "[9, 10]",
  "flattenMode": "shallow",
  "unique": false
}
  }
}
Chain multiple tools in one session with a comma-separated toolId list, e.g. /mcp/sse?toolId=png-to-webp,jpg-to-webp,gif-to-webp (max 20).

Questions or issues? Contact [email protected]

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select
No
—
uniquecheckboxNo—

Response format

Text result

JSON
{
  "result": "Processed text content",
  "error": "Error message (optional)",
  "message": "Notification message (optional)",
  "metadata": {
    "key": "value"
  }
}