What metrics does this MCP support?

It supports Cosine, Dot Product, and Euclidean similarity metrics.

How does this help prevent AI hallucinations?

It acts as a gatekeeper by ensuring only context that meets a strict mathematical similarity threshold is passed to your agent.

Does it handle vector dimension mismatches?

Yes, the validate_vector_format capability checks for dimension compatibility before you perform calculations.

What kind of precision does it use?

All calculations use IEEE 754 floating-point precision.

Can I use this with Claude or Cursor?

Yes, it works with any MCP-compatible client including Claude, Cursor, and Windsurf.

What metrics are supported for similarity calculation?

The calculate_similarity capability supports Cosine, Dot Product, and Euclidean metrics.

How can I ensure my vectors are compatible before calculation?

You should use the validate_vector_format capability to verify that all arrays have identical dimensions and contain only valid numbers.

What happens if a similarity score is below my required threshold?

You can use check_threshold_violation to identify the exact severity of the violation and how far the score has fallen below your target.

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Connect Vector Similarity Threshold Enforcer 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. Stop hallucinations by enforcing strict mathematical relevance standards.

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.

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.

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.

Curated set · 3 capabilities

We ship only the actions that matter.

Capability set01 / 01

01-03

3 capabilities in this set.

Part of 3 available through Vector Similarity Threshold Enforcer.

01
Calculate similarity
This capability computes a specific similarity score between two vectors using your chosen metric. You can specify the metric type and an optional threshold for the calculation.
02
Check threshold violation
This capability identifies exactly how far a similarity score has fallen below your required standard. It helps your agent decide if a piece of context is too weak to use.
03
Validate vector format
This capability performs a pre-flight check to ensure your vectors are compatible before you attempt any math. It prevents errors by verifying vector dimensions match.

Real work

What teams actually run

RAG Pipeline Guardrails

Your agent checks if retrieved chunks meet a minimum similarity score before reading them.

Embedding Validation

You verify that new embeddings are compatible with your existing vector space.

Similarity Auditing

You measure how much a specific score deviates from your target threshold.

Data Integrity Checks

The agent uses the capability to ensure vector data is formatted correctly for downstream tasks.

One connector, every AI

Vector Similarity Threshold Enforcer 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.

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

You don't need a client to put Vector Similarity Threshold Enforcer 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

735ms average. Fast in production.

Vector Similarity Threshold Enforcer is checked daily against the live service.

Daily averagePeak 806ms

Oct 3Today

Fastest day: 542ms
Slowest day: 806ms
14-day trend: Stable0%

Connect your client

One URL. Every client.

Activate the Connector, copy your link, and paste it into the client you already use. 3 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 Vector Similarity Threshold Enforcer, so you can see the experience inside your AI.

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

Vector Similarity Threshold Enforcer Connector

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

Connector linkhttps://edge.vinkius.com/vk_preview_p65G2fMdRHUxodtQJTnDoaNsrYCK9IP8sJx5yPCE/mcp

Claude Desktop

Follow the steps below to connect in seconds.

1In Claude Desktop, open Settings → Connectors.
2Click “Add custom connector” and paste the connector link above as the remote MCP server URL.
3Click Add and start a new chat — Vector Similarity Threshold Enforcer capabilities are ready to use.

Configuration · claude_desktop_config.jsonCopy

{
  "mcpServers": {
    "vector-similarity-threshold-enforcer-mcp": {
      "url": "https://edge.vinkius.com/vk_preview_p65G2fMdRHUxodtQJTnDoaNsrYCK9IP8sJx5yPCE/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 Vector Similarity Threshold Enforcer in Claude and other AIs

See all the AI clients this connector works with ↑

Who it's for

Built for the work Vector Similarity Threshold Enforcer owners hand off.

This is built for engineers building high-precision retrieval systems where accuracy is non-negotiable.

01
AI Engineers
They hand off vector math and threshold logic to the MCP to clean up RAG outputs.
02
Data Scientists
They use the capability to validate embedding similarity during model testing.
03
MLOps Engineers
They implement these checks to prevent garbage data from entering production pipelines.

FAQ

Questions Vector Similarity Threshold Enforcer owners ask.

01
What metrics does this MCP support?
It supports Cosine, Dot Product, and Euclidean similarity metrics.
02
How does this help prevent AI hallucinations?
It acts as a gatekeeper by ensuring only context that meets a strict mathematical similarity threshold is passed to your agent.
03
Does it handle vector dimension mismatches?
Yes, the validate_vector_format capability checks for dimension compatibility before you perform calculations.
04
What kind of precision does it use?
All calculations use IEEE 754 floating-point precision.
05
Can I use this with Claude or Cursor?
Yes, it works with any MCP-compatible client including Claude, Cursor, and Windsurf.
06
What metrics are supported for similarity calculation?
The calculate_similarity capability supports Cosine, Dot Product, and Euclidean metrics.
07
How can I ensure my vectors are compatible before calculation?
You should use the validate_vector_format capability to verify that all arrays have identical dimensions and contain only valid numbers.
08
What happens if a similarity score is below my required threshold?
You can use check_threshold_violation to identify the exact severity of the violation and how far the score has fallen below your target.

Explore

More in Mathematics

Connect Vector Similarity Threshold Enforcer to Claude, Cursor, ChatGPT & more

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

Each Connector runs inside its own sealed environment.

One switch stops every agent. Instantly.

Spending limits that pause instead of surprise.

We ship only the actions that matter.

01-03

Calculate similarity

Check threshold violation

Validate vector format

What teams actually run

RAG Pipeline Guardrails

Embedding Validation

Similarity Auditing

Data Integrity Checks

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

Vector Similarity Threshold Enforcer Connector

Claude Desktop

AI Engineers

Data Scientists

MLOps Engineers

What metrics does this MCP support?

How does this help prevent AI hallucinations?

Does it handle vector dimension mismatches?

What kind of precision does it use?

Can I use this with Claude or Cursor?

What metrics are supported for similarity calculation?

How can I ensure my vectors are compatible before calculation?

What happens if a similarity score is below my required threshold?

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