Text Processing
Calculate similarity percentage between two texts using multiple algorithms including Cosine Similarity, Jaccard Similarity, and Levenshtein Distance
Call this tool from your code in three languages.
curl -X POST 'https://api.elysiatools.com/en/api/tools/text-similarity-detector' \
-H 'Content-Type: application/json' \
-d '{"text1":"Enter the first text to compare...","text2":"Enter the second text to compare...","algorithm":"combined","caseSensitive":false,"ignoreWhitespace":true,"minWordLength":2}'Send a POST request with your inputs as JSON. File parameters require a separate upload first.
POST https://api.elysiatools.com/en/api/tools/text-similarity-detector| Name | Type | Required | Description |
|---|---|---|---|
| text1 | textarea | Yes | — |
| text2 | textarea | Yes | — |
| algorithm | select | Yes | — |
| caseSensitive | checkbox | No | Treat uppercase and lowercase as different characters |
| ignoreWhitespace | checkbox | No | Remove extra spaces, tabs, and newlines before comparison |
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-text-similarity-detector": {
"name": "text-similarity-detector",
"description": "Calculate similarity percentage between two texts using multiple algorithms including Cosine Similarity, Jaccard Similarity, and Levenshtein Distance",
"baseUrl": "https://api.elysiatools.com/mcp/sse?toolId=text-similarity-detector",
"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": "text-similarity-detector",
"arguments": {
"text1": "Enter the first text to compare...",
"text2": "Enter the second text to compare...",
"algorithm": "combined",
"caseSensitive": false,
"ignoreWhitespace": true,
"minWordLength": 2
}
}
}Questions or issues? Contact [email protected]
| minWordLength | number | No | Ignore words shorter than this length |
Text result
{
"result": "Processed text content",
"error": "Error message (optional)",
"message": "Notification message (optional)",
"metadata": {
"key": "value"
}
}