Overview
Lemmatizer works by analyzing the text provided and lemmatizing it. It uses advanced algorithms to lemmatize the text and returns the lemmatized text.
Live Test Text Lemmatizer Skill Skill →
The tool
Once your client is connected to the VerveKit server, this appears in its tool list as TextLemmatizerSkill. 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": "TextLemmatizerSkill",
"arguments": {
"text": "The dogs were running and jumping over the fences"
}
}You do not name the tool yourself; the model picks it. Asking about The dogs were running and jumping over the fences in the terms this skill covers is enough for it to reach for TextLemmatizerSkill 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/mcpPer-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 |
|---|---|---|
textRequired | string | The text to lemmatize |
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.
{
"status": "ok",
"error": null,
"data": {
"found": 3,
"lemmas": {
"cat": 1,
"ran": 1,
"door": 2
}
}
}
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 |
|---|---|---|---|
found | number | 3 | The number of lemmas found in the input text |
lemmasPremium | object | {…} | Each word in the text mapped to its lemma, keyed by the word as it appeared |
lemmas.catPremium | number | 1 | |
lemmas.ranPremium | number | 1 | |
lemmas.doorPremium | number | 2 |
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 2 credits each time the tool actually runs; a model that reasons about the tool without calling it costs nothing.
Use cases
- Search Query Normalization
- Search engines convert user queries into root words to match plural or conjugated variations against indexed product titles.
- Customer Feedback Clustering
- Group incoming support tickets by topic after reducing verbs and nouns to base forms for cleaner text classification.
- Vocabulary Learning Systems
- Language learning platforms detect inflected verb forms across reading exercises and map them back to headwords in student flashcards.
- SEO Content Auditing
- Before scanning articles for keyword density, content marketing platforms strip grammatical suffixes to measure true term frequencies across drafts.
Other ways to use Text Lemmatizer Skill
Set up Text Lemmatizer 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: