How does this MCP help with token limits?

It uses a deterministic engine to select the best subset of information that fits within your specified token budget.

Can I use this with Claude or Cursor?

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

What kind of selection strategies are available?

You can use Top-K, Relevance Threshold, or Diversity-Based selection strategies to pick your chunks.

Does this replace my vector database?

No, it sits between your retrieval step and your AI client to optimize the data you've already retrieved.

How do I know if my chunk selection is good?

You can use the calculate_metrics capability to check information density and coverage scores.

What are the different selection strategies?

The engine supports Top-K (picking the N most relevant), Relevance Threshold (picking chunks above a specific score), and Diversity-Based (maximizing unique sources within the budget).

How does the optimizer handle token limits?

The select_chunks capability accepts a contextBudgetTokens parameter. The engine ensures the sum of tokenCount for all selected chunks does not exceed this limit.

Can I remove redundant information?

Yes, you can use the deduplicate_chunks capability to identify and merge overlapping chunks, which helps in reducing noise and saving tokens.

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Connect RAG Chunk Selection Optimizer 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. Keep your AI within token limits without losing context.

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 RAG Chunk Selection Optimizer.

01
Calculate metrics
This capability evaluates the quality of your selected chunks by measuring information density, coverage, and marginal value.
02
Deduplicate chunks
Use this to clean up your retrieved data by merging or removing redundant information.
03
Select chunks
This capability picks the best chunks from a retrieved set using strategies like Top-K, relevance thresholds, or diversity-based selection.

Real work

What teams actually run

Long Document Analysis

An agent processes a massive PDF by selecting only the most relevant sections to fit within a single prompt.

Cost Management

You limit the number of tokens sent to an LLM to keep API costs predictable and low.

Reducing Hallucinations

By removing noisy or repetitive chunks, you provide the agent with cleaner, more reliable facts.

Multi-Source Retrieval

Your agent merges and deduplicates information pulled from multiple different databases.

One connector, every AI

RAG Chunk Selection Optimizer 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 RAG Chunk Selection Optimizer 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

847ms average. Fast in production.

RAG Chunk Selection Optimizer is checked daily against the live service.

Daily averagePeak 972ms

Oct 3Today

Fastest day: 731ms
Slowest day: 972ms
14-day trend: Improving-15%

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 RAG Chunk Selection Optimizer, so you can see the experience inside your AI.

It does not authenticate your account with RAG Chunk Selection Optimizer. Actions requiring credentials or live account data may not run until you activate the Connector and authorize the service.

RAG Chunk Selection Optimizer Connector

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

Connector linkhttps://edge.vinkius.com/vk_preview_MUkr0meo1CTgmH0DxXIUM43TITGllkXwBnnhs3Kv/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 — RAG Chunk Selection Optimizer capabilities are ready to use.

Configuration · claude_desktop_config.jsonCopy

{
  "mcpServers": {
    "rag-chunk-selection-optimizer-mcp": {
      "url": "https://edge.vinkius.com/vk_preview_MUkr0meo1CTgmH0DxXIUM43TITGllkXwBnnhs3Kv/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 RAG Chunk Selection Optimizer in Claude and other AIs

See all the AI clients this connector works with ↑

Who it's for

Built for the work RAG Chunk Selection Optimizer owners hand off.

This MCP is built for developers and engineers building production-grade RAG systems who need to control token costs and accuracy.

01
AI Engineers
They hand off raw retrieval results to this MCP to refine the context before it hits the model.
02
LLM Developers
They use it to optimize the efficiency of agentic workflows and long-context reasoning.
03
Data Engineers
They implement this to manage the flow of information between vector databases and AI clients.

FAQ

Questions RAG Chunk Selection Optimizer owners ask.

01
How does this MCP help with token limits?
It uses a deterministic engine to select the best subset of information that fits within your specified token budget.
02
Can I use this with Claude or Cursor?
Yes, you can connect this MCP to any MCP-compatible client including Claude, Cursor, and Windsurf.
03
What kind of selection strategies are available?
You can use Top-K, Relevance Threshold, or Diversity-Based selection strategies to pick your chunks.
04
Does this replace my vector database?
No, it sits between your retrieval step and your AI client to optimize the data you've already retrieved.
05
How do I know if my chunk selection is good?
You can use the calculate_metrics capability to check information density and coverage scores.
06
What are the different selection strategies?
The engine supports Top-K (picking the N most relevant), Relevance Threshold (picking chunks above a specific score), and Diversity-Based (maximizing unique sources within the budget).
07
How does the optimizer handle token limits?
The select_chunks capability accepts a contextBudgetTokens parameter. The engine ensures the sum of tokenCount for all selected chunks does not exceed this limit.
08
Can I remove redundant information?
Yes, you can use the deduplicate_chunks capability to identify and merge overlapping chunks, which helps in reducing noise and saving tokens.

Explore

More in Optimization

Connect RAG Chunk Selection Optimizer 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 metrics

Deduplicate chunks

Select chunks

What teams actually run

Long Document Analysis

Cost Management

Reducing Hallucinations

Multi-Source Retrieval

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

RAG Chunk Selection Optimizer Connector

Claude Desktop

AI Engineers

LLM Developers

Data Engineers

How does this MCP help with token limits?

Can I use this with Claude or Cursor?

What kind of selection strategies are available?

Does this replace my vector database?

How do I know if my chunk selection is good?

What are the different selection strategies?

How does the optimizer handle token limits?

Can I remove redundant information?

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