Text Similarity Skill

Overview

Text Similarity works by comparing the two texts and calculating the similarity score based on various factors. It returns the similarity score in a structured format.

Live Test Text Similarity Skill Skill →

The tool

Once your client is connected to the VerveKit server, this appears in its tool list as TextSimilaritySkill. It is read-only and open-world — it fetches and never mutates anything on your side — so most clients call it without asking you to confirm.

{
  "name": "TextSimilaritySkill",
  "arguments": {
    "text1": "The quick brown fox jumps over the lazy dog",
    "text2": "A fast brown fox leaps over a sleepy dog"
  }
}

You do not name the tool yourself; the model picks it. Asking about The quick brown fox jumps over the lazy dog in the terms this skill covers is enough for it to reach for TextSimilaritySkill on its own — naming it explicitly also works, and is the way to force the call.

Connecting

One server URL covers every skill in the catalog, including this one. Authorization is OAuth: the client opens a browser once, and there is no key to paste into a config file.

{
  "mcpServers": {
    "vervekit": {
      "url": "https://api.vervekit.com/v1/mcp"
    }
  }
}

https://api.vervekit.com/v1/mcp

Per-client setup — Claude, Cursor, VS Code, ChatGPT — is on the MCP setup page.

Arguments

These are the properties on the tool's inputSchema, so a well-behaved client validates them before the call is made. Premium arguments are accepted on every plan but only take effect on plans that include them.

What the model gets back

The result carries a structuredContent object matching the tool's declared outputSchema, so a client reads fields without parsing prose. status is "ok" and error is null on success; a null field means the value was not available for that input, not that the call failed.

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{
  "status": "ok",
  "error": null,
  "data": {
    "simiarity": 45.83,
    "similarityCaseSensitive": 47.22,
    "difference": {
      "count": 7,
      "percentage": 58.33
    }
  }
}

Response fields

Paths are relative to data. Premium fields are absent rather than zeroed on plans that do not include them, so check for presence instead of comparing to 0.

Failure modes

Errors come back as tool errors carrying a sentence the model can act on, not a bare status code. Error handling covers the full list.

A call costs 2 credits each time the tool actually runs; a model that reasons about the tool without calling it costs nothing.

Use cases

Plagiarism Screening

Educators check student assignment submissions against reference essays to catch paraphrased or near-identical passages using case-insensitive similarity scores.

Catalog Deduplication

To merge duplicate inventory listings, marketplace operators evaluate difference percentages across submitted product titles.

Support Ticket Clustering

Helpdesk systems group incoming user inquiries with existing knowledge base answers when match percentages cross predefined thresholds.

Translation Revision Tracking

When editors revise translated copy, localization teams compute case-sensitive similarity to track how much phrasing changed between drafts.

Other ways to use Text Similarity Skill

Set up Text Similarity Skill on VerveKit, or reach the same source a different way. Your VerveKit account and credits work on all of them — one key, one balance.

Related

More in Text Processing: