Keyword Extraction Skill

What this skill does

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. You switch it on with a single toggle — no SDK and no endpoint wiring — and it answers over MCP or REST using the same key as every other skill.

See it work

A real response from the Keyword Extraction Skill — exactly what your agent gets back when it makes the call.

vervekit · keywordextractortool call

{
  "url": "https://en.wikipedia.org/wiki/Email_address"
}

result

url: https://en.wikipedia.org/wiki/Email_addr…
keywordCount: 50
topKeyword: email
totalOccurrences: 672
keywords: { … }
topKeywords: [ 5 items ]

Run it with your own input. Live calls happen in your dashboard, on your key.

Ways to use it

Text tooling without the ML stack

Enable Keyword Extraction and your app can add keyword extraction to your app — translate, analyze, clean — without hosting a model or wiring a language API.

A tool your agent invokes

Over MCP, keywordextractor is the tool your model calls to process text mid-task, on the same connection as every other skill.

Batch it from your backend

Call Keyword Extraction over REST to add keyword extraction to your app across a whole queue of text in your own server-side job.

Use it in your app

Once enabled, this skill is reachable two ways — pick whichever fits how you build. Both use the same key.

For AI agentsMCP

It appears to your model as a callable tool. No extra code — the agent invokes it when a task needs it.

keywordextractor

For apps & backendsREST

Call it from your server with one request and your key. Node, Python, Go — anything that can send a GET.

GET /v1/keywordextractor

Ask your agent

“Enable Keyword Extraction and ask your agent to add keyword extraction to your app.”
“Clean this text up and tell me what's in it.”

Keyword Extraction Skill, answered

How to connect it over MCP or REST.

How do I add Keyword Extraction to my app or agent?

Enable the Keyword Extraction Skill on VerveKit, then reach it two ways with the same key: over MCP (it appears to your agent as the keywordextractor tool) or over REST (call it from any backend). No SDK to install and no endpoint to wire.

MCP or REST — which should I use?

Both work off one key. Use MCP when an AI agent should decide when to add keyword extraction to your app — the skill shows up as a callable tool. Use REST when your own server-side code should call it directly. Many apps use both.

Which agents and frameworks does it work with?

Any MCP client — Claude, Cursor, LangChain, and custom agents all speak the Model Context Protocol, so Keyword Extraction appears as a standard tool with nothing skill-specific to integrate.

How many credits does a Keyword Extraction call cost?

Each call costs 5 credits. Every skill rides the same key and the same connection, so enabling more skills doesn't add more integrations to manage.

Do I need to install anything?

No SDK and no endpoint wiring — enabling the skill is a toggle. You point your agent at the VerveKit MCP endpoint (or call REST) and Keyword Extraction is available immediately.

Where does the data come from, and what shows on my bill?

VerveKit runs on APIVerve, our production data engine; Keyword Extraction is one of 300+ skills on the same key. Invoices and card statements show APIVERVE.

Text tooling without the ML stack

A tool your agent invokes

Batch it from your backend

Keyword Extraction Skill, answered

Give your software a way to act on the world.

Scaling in production?