HTML to Text Skill

Overview

HTML to Text works by parsing the HTML content and extracting the text. Text extraction is done by removing the HTML tags and returning the plain text content. Advanced algorithms are used to ensure accurate text extraction.

Live Test HTML to Text Skill Skill →

The tool

Once your client is connected to the VerveKit server, this appears in its tool list as HTMLtoTextSkill. 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": "HTMLtoTextSkill",
  "arguments": {
    "html": "<html><body><h1>Welcome</h1><p>This is a paragraph of text.</p><ul><li>Item 1</li><li>Item 2</li></ul></body></html>"
  }
}

You do not name the tool yourself; the model picks it. Asking about <html><body><h1>Welcome</h1><p>This is a paragraph of text.</p><ul><li>Item 1</li><li>Item 2</li></ul></body></html> in the terms this skill covers is enough for it to reach for HTMLtoTextSkill 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": {
    "text": "This is an example paragraph. Anything in the body tag will appear on the page, just like this p tag and its contents.",
    "parsed": true,
    "extractionMethod": "article",
    "detectedLanguage": {
      "language": "english",
      "confidence": 0.3507446808510638
    },
    "characterCount": 118,
    "wordCount": 23
  }
}

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

Inbound Email Parsing

Strip HTML markup from customer support emails before passing unformatted message bodies into ticket triage queues.

Search Engine Indexing

When crawling web pages, remove formatting tags and extract readable text to compute accurate word counts for search indices.

LLM Prompt Preparation

To minimize prompt token counts, data engineers strip raw article markup down to readable text before querying language models.

Feed Snippet Generation

Content aggregators convert rich blog markup into plain text snippets for previews, push notifications, and mobile readers.

Other ways to use HTML to Text Skill

Set up HTML to Text 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

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