Keyword Extraction Skill

The tool

Let your agent pull keywords from text or a URL. Call it as an MCP tool to return the top terms and their frequency for any content.

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

{
  "name": "KeywordExtractionSkill",
  "arguments": {
    "text": "Machine learning is a subset of artificial intelligence that enables systems to learn from data. Deep learning uses neural networks to process complex patterns in data."
  }
}

What that looks like in a conversation

You don't name the tool — the model picks it. Asking about Machine learning is a subset of artificial intelligence that enables systems to learn from data. Deep learning uses neural networks to process complex patterns in data. in the terms this source covers is enough for it to reach for KeywordExtractionSkill on its own.

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 Desktop, 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.

What the model gets back

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

{
  "status": "ok",
  "error": null,
  "data": {
    "url": "https://en.wikipedia.org/wiki/Email_address",
    "keywordCount": 50,
    "topKeyword": "email",
    "totalOccurrences": 672,
    "keywords": {
      "email": 94,
      "address": 61,
      "mail": 52,
      "domain": 34,
      "addresses": 34,
      "characters": 27,
      "retrieved": 27,
      "internet": 17,
      "message": 15,
      "validation": 12
    },
    "topKeywords": [
      {
        "keyword": "email",
        "count": 94,
        "percentage": 14
      },
      {
        "keyword": "address",
        "count": 61,
        "percentage": 9.1
      },
      {
        "keyword": "mail",
        "count": 52,
        "percentage": 7.7
      },
      {
        "keyword": "domain",
        "count": 34,
        "percentage": 5.1
      },
      {
        "keyword": "addresses",
        "count": 34,
        "percentage": 5.1
      }
    ]
  }
}

Fields

Failure modes

Errors come back as tool errors with a sentence the model can act on, not a bare status code.

Other ways to use Keyword Extraction Skill

Set up Keyword Extraction 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.

Frequently asked questions