Sentiment Analysis Skill

What this skill does

Let your agent read tone. Call it as an MCP tool to score any text's sentiment and return a positive, negative or neutral label. 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 Sentiment Analysis Skill — exactly what your agent gets back when it makes the call.

vervekit · sentimentanalysistool call

{
  "text": "I'm so excited that tomorrow is going to be sunny! Can't wait!"
}

result

comparative: 0.25
sentimentText: positive
sentiment: 3
isPositive: true
isNegative: false
normalizedScore: 0.3

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

Ways to use it

One toggle, a new capability

Enable Sentiment Analysis and your app or agent can analyze sentiment on demand — no SDK, no endpoint wiring. It answers over MCP or REST using the same key as every other skill.

A tool agents call themselves

Over MCP, Sentiment Analysis appears to your model as the sentimentanalysis tool it invokes whenever a task needs it — you don't write the glue, the agent reaches for it.

REST from any backend

Prefer to call it directly? Any language that can send a GET can hit Sentiment Analysis to analyze sentiment inline in your own server-side code.

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.

sentimentanalysis

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/sentimentanalysis

Ask your agent

“Enable Sentiment Analysis and ask your agent to analyze sentiment.”
“Use the Sentiment Analysis skill for me.”

Sentiment Analysis Skill, answered

How to connect it over MCP or REST.

How do I add Sentiment Analysis to my app or agent?

Enable the Sentiment Analysis Skill on VerveKit, then reach it two ways with the same key: over MCP (it appears to your agent as the sentimentanalysis 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 analyze sentiment — 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 Sentiment Analysis appears as a standard tool with nothing skill-specific to integrate.

How many credits does a Sentiment Analysis 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 Sentiment Analysis is available immediately.

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

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

One toggle, a new capability

A tool agents call themselves

REST from any backend

Sentiment Analysis Skill, answered

Give your software a way to act on the world.

Scaling in production?