Antonym Finder Skill

Overview

Submit a target word and an optional language parameter, which defaults to English. The endpoint checks the term to return an array of direct antonyms. It also identifies the grammatical part of speech, omitting that field whenever an input word is unrecognised.

Live Test Antonym Finder Skill Skill →

The tool

Once your client is connected to the VerveKit server, this appears in its tool list as AntonymFinderSkill. 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.

Tool call

{
  "name": "AntonymFinderSkill",
  "arguments": {
    "word": "happy"
  }
}

You do not name the tool yourself; the model picks it. Asking about happy in the terms this skill covers is enough for it to reach for AntonymFinderSkill 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"
    }
  }
}

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.

Argument	Type	Description
`word`Required	string	The word to get the antonyms for
`language`Optional	string	Language to return the antonyms in. Defaults to English

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.

Result

{
  "status": "ok",
  "error": null,
  "data": {
    "partOfSpeech": "adjective",
    "word": "happy",
    "language": "en",
    "antonyms": [
      "sad",
      "unhappy",
      "miserable",
      "depressed",
      "glum"
    ]
  }
}

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.

Field	Type	Example	Description
`partOfSpeech`	string	`adjective`	Part of speech of the input word (noun, verb, adjective, adverb and so on). Omitted when the word is not recognised.
`word`	string	`happy`	The input word for which antonyms were found
`language`	string	`en`	Language code identifying the language of the word
`antonyms`	array	`["sad","unhappy","miserable"]`	Array of antonym words with opposite meanings

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.

Status	What it means
`400` / `422`	The arguments did not validate. The message names the offending one.
`401`	The OAuth session is invalid or expired — reconnect the server.
`403`	Blocked by a key restriction or an IP allow-list. Never a bad identity.
`404`	This skill is not part of VerveKit. Check the catalog.
`429`	Out of credits, or a brief rate limit. The message tells them apart.

A call costs 20 credits each time the tool actually runs; a model that reasons about the tool without calling it costs nothing.

Use cases

Vocabulary Flashcard Building: EdTech platforms show opposite terms next to word definitions to help students prepare for standardized language exams.
Content Rewriting Assistance: In text editors, writing assistants suggest contrasting phrasing by retrieving antonyms and parts of speech for highlighted text.
Automated Quiz Generation: To generate fill-in-the-blank questions automatically, course creators query antonyms to build multiple-choice distractors for reading tests.
Customer Review Analysis: When evaluating customer reviews, text processing pipelines inspect antonym lists to detect negation and tonal shifts.

Other ways to use Antonym Finder Skill

Set up Antonym Finder 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.

Call it as a REST APIOne HTTPS endpoint and an x-api-key header, with SDKs for Node, Python and .NET.APIVerve →Reference →

Google Sheets or ExcelA =VERVE() formula fills a column — no script, no export, recalculates in place.VerveSheets →Reference →