Text Embeddings Skill

What this skill does

Text Embeddings turns a sentence, paragraph or short document into a 384-dimension vector that captures its meaning, ready for semantic search, clustering, deduplication and recommendations. Send one text or a batch of up to 32, and add a compare text to get the cosine similarity of each one to it in the same call. 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 Text Embeddings Skill — exactly what your agent gets back when it makes the call.

vervekit · textembeddingstool call

{
  "text": "How do I reset my password?",
  "compare": "I forgot my login credentials"
}

result

model: all-MiniLM-L6-v2
dimensions: 384
normalized: true
embedding: [ 384 items ]
characters: 27
similarity: 0.6762

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 Text Embeddings and your app or agent can add text embeddings to your app 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, Text Embeddings appears to your model as the textembeddings 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 Text Embeddings to add text embeddings to your app 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.

textembeddings

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/textembeddings

Ask your agent

“Enable Text Embeddings and ask your agent to add text embeddings to your app.”
“Use the Text Embeddings skill for me.”

Text Embeddings Skill, answered

How to connect it over MCP or REST.

How do I add Text Embeddings to my app or agent?

Enable the Text Embeddings Skill on VerveKit, then reach it two ways with the same key: over MCP (it appears to your agent as the textembeddings 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 text embeddings 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 Text Embeddings appears as a standard tool with nothing skill-specific to integrate.

How many credits does a Text Embeddings 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 Text Embeddings is available immediately.

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

VerveKit runs on APIVerve, our production data engine; Text Embeddings 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

Text Embeddings Skill, answered

Other ways to use Text Embeddings

Give your software a way to act on the world.

Scaling in production?