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Connect Embedding Similarity Calculator to your AI

Set it up once and it works in ChatGPT, Claude, Cursor, and any other client you love. It all runs safely in the background, so you can relax and focus on what really matters. Get precise distance and similarity scores for multidimensional data.

Ask AI about this Connector

Developed, maintained, and hosted by Vinkius.

MCP VERIFIED · PRODUCTION READY · VINKIUS GUARANTEED

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Works with modern AI clients that support MCP, including ChatGPT, Claude, Cursor, and more.

ChatGPTClaudeCursorPerplexityGeminiMicrosoft CopilotRaycastMeta AI

Your AI does the work. We keep it safe and sound.

No other AI tool gives you this. Your workflow runs in the client you already love, your account stays protected, and you decide what the agent can and cannot do. It is the whole reason to make the switch.

Each Connector lives inside our infrastructure, the same place where your workflow runs. The three things below are already part of it, working quietly in the background so you never have to think about them. Nothing to add, nothing to set up, nothing to worry about.

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V8 Isolate01

Each Connector runs inside its own sealed environment.

Each Connector runs inside its own sealed V8 runtime — no access to your files, your system, or other servers. A Connector never sees past its own request.

Kill Switch02

One switch stops every agent. Instantly.

One action deactivates every Connector, revokes every token, and ends every session at once. AI Governance keeps the record.

FinOps per User03

Spending limits that pause instead of surprise.

Every user keeps their own connections and credentials, isolated from everyone else. Anything that would cross your spending limit is held for your approval, not billed.

Three controls, built into the layer your AI connects through.

One connector, every AI

Embedding Similarity Calculator works with the most popular AI clients.

These are the most popular clients, each with a step-by-step guide: one link, set up once, with governance and visibility built in. And because everything runs on the MCP standard, the same connection also works in any other compatible client — nothing to rebuild.

ClaudeClaudeChatGPTChatGPTGeminiGeminiPerplexityPerplexityGrokGrokMicrosoft CopilotMicrosoft CopilotCursorCursorVS CodeVS CodeWindsurfWindsurfJetBrainsJetBrainsClineClineLangChainLangChainVercel AI SDKVercel AI SDKLovableLovableZ.aiZ.aiRaycastRaycastQwen CodeQwen CodeKimi CodeKimi CodeLe ChatLe Chat

Building your own app? The connector is yours to use.

You don't need a client to put Embedding Similarity Calculator to work: the same hosted connection plugs into your own applications and agent code, with the same governance on every request. Build with it, chat with it — one connection for both.

Observed, not estimated

630ms average. Fast in production.

Embedding Similarity Calculator is checked daily against the live service.

Daily averagePeak 889ms
Oct 3Today
Fastest day
514ms
Slowest day
889ms
14-day trend
Improving-7%

Connect your client

One URL. Every client.

Activate the Connector, copy your link, and paste it into the client you already use. 0 capabilities arrive ready to run.

Preview access · not provider authentication

The vk_preview_* token belongs to Vinkius preview infrastructure. It lets Claude discover and display the capabilities of Embedding Similarity Calculator, so you can see the experience inside your AI.

It does not authenticate your account with Embedding Similarity Calculator. Actions requiring credentials or live account data may not run until you activate the Connector and authorize the service.

Embedding Similarity Calculator Connector

You're all set. Choose your MCP client and follow the setup instructions.

Connector linkhttps://edge.vinkius.com/vk_preview_Jw1zV0EdxZijh9ns0Ve4DwNFVTYvTKlOlQyDwkeV/mcp

Claude Desktop

Follow the steps below to connect in seconds.

  1. 1In Claude Desktop, open Settings → Connectors.
  2. 2Click “Add custom connector” and paste the connector link above as the remote MCP server URL.
  3. 3Click Add and start a new chat — Embedding Similarity Calculator capabilities are ready to use.
Configuration · claude_desktop_config.jsonCopy
{
  "mcpServers": {
    "embedding-similarity-calculator-mcp": {
      "url": "https://edge.vinkius.com/vk_preview_Jw1zV0EdxZijh9ns0Ve4DwNFVTYvTKlOlQyDwkeV/mcp"
    }
  }
}
  • Claude
  • ChatGPT
  • Cursor
  • VS Code
  • Windsurf
  • Claude Code
  • JetBrains
  • Cline

Step-by-step instructions for each client are in the guide. How to connect

Guided setup for Claude? Set up Embedding Similarity Calculator in Claude and other AIs

See all the AI clients this connector works with ↑

Who it's for

Built for the work Embedding Similarity Calculator owners hand off.

This MCP is built for technical users working with high-dimensional data and embeddings.

  • 01

    Machine Learning Engineers

    Hand off vector comparison tasks to the agent to verify embedding relationships.

  • 02

    Data Scientists

    Use the agent to quickly rank or validate large datasets of numerical vectors.

  • 03

    AI Developers

    Offload precise mathematical distance calculations from the LLM to this specialized capability.

FAQ

Questions Embedding Similarity Calculator owners ask.

  • 01

    What metrics can this MCP calculate?

    It calculates cosine similarity, Euclidean distance, dot product, and Manhattan distance.

  • 02

    Can I use this with Claude?

    Yes, you can connect this MCP to any MCP-compatible client like Claude, Cursor, or Windsurf.

  • 03

    How does it handle large lists of vectors?

    It uses the ranking capability to sort through candidate vectors based on your chosen metric.

  • 04

    What happens if my vectors have different dimensions?

    The validation capability will identify that the dimensions are incompatible for group operations.

  • 05

    Do I need to host this myself?

    No, Vinkius hosts and manages the MCP so it is ready to use immediately after connection.

  • 06

    What metrics are supported?

    The calculator supports Cosine Similarity, Euclidean Distance, Dot Product Similarity, and Manhattan Distance. Capabilities available: your_tool_name.

  • 07

    Do all vectors need to have the same size?

    Yes, for any mathematical comparison to be valid, all vectors involved must share the exact same dimensionality.

  • 08

    How does ranking work for distance metrics?

    For distance-based metrics like Euclidean and Manhattan, vectors with smaller values are ranked higher. For similarity-based metrics like Cosine and Dot Product, larger values are ranked higher.

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