How does this MCP help with vector database scaling?

It allows you to estimate the memory footprint and search performance for different scales, helping you plan your hardware needs before deployment.

Which algorithms are supported?

The MCP provides performance modeling for HNSW and IVF algorithms.

Can I use this to find the best efSearch value?

Yes, you can use the parameter recommendation capability to find optimal ranges for values like efSearch based on your target recall.

Do I need to host the MCP myself?

No, Vinkius hosts and manages the MCP, so you just connect your client and start using the capabilities.

What kind of data do I need to provide for memory estimates?

You need to provide the vector count, the number of dimensions, and the precision tier you are using.

What algorithms are supported?

The server supports HNSW (Hierarchical Navigable Small World) and IVF (Inverted File Index) algorithms.

How can I estimate the RAM needed for my index?

Use the calculate_memory_usage capability. You will need to provide the total vector count, dimensions per vector, and the precision tier (float32, float16, or int8).

Can I get parameter suggestions for a specific recall target?

Yes, the get_parameter_recommendations capability provides suggested ranges for parameters like efSearch or nprobe to help you reach your target recall percentage.

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Connect Vector Index Recall Estimator to ChatGPT and Claude

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. Predict index performance and hardware requirements before you deploy.

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 Index Estimator.

01
Calculate memory usage
This capability estimates the total memory footprint required for a specific vector index. It uses your vector count, dimensions, and precision tier to provide a hardware requirement estimate.
02
Estimate search performance
This capability generates performance tradeoff curves for ANN algorithms. It maps out the relationship between recall and latency for HNSW and IVF implementations.
03
Get parameter recommendations
This capability suggests optimal configuration ranges for your index. It identifies specific values like efSearch or nprobe needed to meet a target recall percentage for your dataset size.

Real work

What teams actually run

Infrastructure Sizing

Determine if your current server has enough RAM to hold a new vector index before you attempt to build it.

Tuning HNSW Indices

Find the right efSearch value to balance search speed against accuracy requirements.

IVF Configuration

Calculate the necessary nprobe range to hit specific recall targets for large datasets.

Performance Benchmarking

Model how search latency will change as you adjust your index parameters.

One connector, every AI

Vector Index Estimator 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 Index Estimator 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

704ms average. Fast in production.

Vector Index Estimator is checked daily against the live service.

Daily averagePeak 803ms

Oct 3Today

Fastest day: 542ms
Slowest day: 803ms
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 Index Estimator, so you can see the experience inside your AI.

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

Vector Index Estimator Connector

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

Connector linkhttps://edge.vinkius.com/vk_preview_N33Tt9r0kNlk9h9CQjDLut4LHlCfuA7wPCcdfONP/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 Index Estimator capabilities are ready to use.

Configuration · claude_desktop_config.jsonCopy

{
  "mcpServers": {
    "vector-index-recall-estimator-mcp": {
      "url": "https://edge.vinkius.com/vk_preview_N33Tt9r0kNlk9h9CQjDLut4LHlCfuA7wPCcdfONP/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 Index Estimator in Claude and other AIs

See all the AI clients this connector works with ↑

Who it's for

Built for the work Vector Index Estimator owners hand off.

This MCP is built for engineers managing large scale vector search workloads. They use it to move from theoretical designs to concrete hardware and configuration plans.

01
Machine Learning Engineers
They hand off index tuning and memory estimation tasks to the agent.
02
Data Engineers
They use the capability to plan infrastructure requirements for vector databases.
03
AI Infrastructure Architects
They rely on these estimates to size clusters and optimize search latency.

FAQ

Questions Vector Index Estimator owners ask.

01
How does this MCP help with vector database scaling?
It allows you to estimate the memory footprint and search performance for different scales, helping you plan your hardware needs before deployment.
02
Which algorithms are supported?
The MCP provides performance modeling for HNSW and IVF algorithms.
03
Can I use this to find the best efSearch value?
Yes, you can use the parameter recommendation capability to find optimal ranges for values like efSearch based on your target recall.
04
Do I need to host the MCP myself?
No, Vinkius hosts and manages the MCP, so you just connect your client and start using the capabilities.
05
What kind of data do I need to provide for memory estimates?
You need to provide the vector count, the number of dimensions, and the precision tier you are using.
06
What algorithms are supported?
The server supports HNSW (Hierarchical Navigable Small World) and IVF (Inverted File Index) algorithms.
07
How can I estimate the RAM needed for my index?
Use the calculate_memory_usage capability. You will need to provide the total vector count, dimensions per vector, and the precision tier (float32, float16, or int8).
08
Can I get parameter suggestions for a specific recall target?
Yes, the get_parameter_recommendations capability provides suggested ranges for parameters like efSearch or nprobe to help you reach your target recall percentage.

Explore

More in Analytics

Connect Vector Index Estimator 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 memory usage

Estimate search performance

Get parameter recommendations

What teams actually run

Infrastructure Sizing

Tuning HNSW Indices

IVF Configuration

Performance Benchmarking

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

Vector Index Estimator Connector

Claude Desktop

Machine Learning Engineers

Data Engineers

AI Infrastructure Architects

How does this MCP help with vector database scaling?

Which algorithms are supported?

Can I use this to find the best efSearch value?

Do I need to host the MCP myself?

What kind of data do I need to provide for memory estimates?

What algorithms are supported?

How can I estimate the RAM needed for my index?

Can I get parameter suggestions for a specific recall target?

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