How does this MCP help with RAG costs?

It allows your AI client to calculate the total cost per query and find the most efficient chunking strategies to reduce token spend.

Can I use this to optimize my chunking strategy?

Yes. The optimize_chunking_strategy capability finds the ideal chunk size to balance retrieval accuracy against LLM token costs.

What clients are compatible with this MCP?

You can connect this MCP to any compatible client including Claude, Cursor, Windsurf, and VS Code.

Does this MCP help with latency issues?

Yes. You can use the analyze_latency_impact capability to estimate the additional infrastructure spend required to meet specific latency targets.

How is the data hosted?

Vinkius hosts and manages the MCP, so you don't have to worry about running it locally or managing credentials.

How does this capability help reduce RAG costs?

It identifies the primary cost drivers in your pipeline and suggests optimizations like adjusting chunk sizes or selecting more efficient retrieval strategies using get_optimization_priorities.

Can I model the cost of meeting specific latency SLAs?

Yes, the analyze_latency_impact capability calculates the estimated cost increase required to move from your current latency to a faster target latency.

What metrics are included in the cost breakdown?

The calculate_query_economics capability provides a detailed breakdown including embedding costs, retrieval costs, and LLM inference costs.

An astronaut overseeing a futuristic data hub

Download PDF Markdown Version

Connect RAG Economics Analyzer to ChatGPT, Claude and Gemini

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. Model and control your RAG infrastructure spending.

Ask AI about this Connector

Developed, maintained, and hosted by Vinkius.

MCP VERIFIED · PRODUCTION READY · VINKIUS GUARANTEED

Waiting for input…

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 · 4 capabilities

We ship only the actions that matter.

Capability set01 / 01

01-04

4 capabilities in this set.

Part of 4 available through RAG Economics.

01
Calculate query economics
This capability provides a full cost breakdown for a single user query. It calculates the total expense by looking at all contributing factors.
02
Get optimization priorities
This capability identifies which parts of your RAG stack are driving your costs. It highlights primary and secondary drivers for optimization.
03
Optimize chunking strategy
This capability finds the ideal chunk size for your data. It balances the need for retrieval accuracy against the cost of LLM tokens.
04
Analyze latency impact
This capability estimates the cost of speed. It calculates how much extra infrastructure spend is required to meet a specific latency target.

Real work

What teams actually run

Scaling RAG Systems

Model how costs will scale as your user base grows from hundreds to millions of queries.

Latency vs. Cost Tradeoffs

Determine if the user experience gain from faster responses justifies the increased compute spend.

Parameter Tuning

Find the most efficient chunking strategy to minimize LLM token usage while maintaining accuracy.

Budget Forecasting

Use query economics to predict monthly infrastructure spend based on expected traffic.

One connector, every AI

RAG Economics 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 Economics 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

883ms average. Fast in production.

RAG Economics is checked daily against the live service.

Daily averagePeak 1244ms

Oct 3Today

Fastest day: 685ms
Slowest day: 1244ms
14-day trend: Slowing+9%

Connect your client

One URL. Every client.

Activate the Connector, copy your link, and paste it into the client you already use. 4 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 Economics, so you can see the experience inside your AI.

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

RAG Economics Connector

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

Connector linkhttps://edge.vinkius.com/vk_preview_bW0DihmAVuNSYlddUrnmK4QjLjMyKslDRonEcVzO/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 Economics capabilities are ready to use.

Configuration · claude_desktop_config.jsonCopy

{
  "mcpServers": {
    "rag-economics-analyzer-mcp": {
      "url": "https://edge.vinkius.com/vk_preview_bW0DihmAVuNSYlddUrnmK4QjLjMyKslDRonEcVzO/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 Economics in Claude and other AIs

See all the AI clients this connector works with ↑

Who it's for

Built for the work RAG Economics owners hand off.

This MCP is built for technical teams managing production-scale RAG applications who need to treat AI infrastructure as a predictable line item.

01
AI Engineers
They use the capability to tune chunking and retrieval parameters for better efficiency.
02
MLOps Engineers
They use it to model how infrastructure changes affect the total cost of ownership.
03
Product Managers
They use the data to understand the unit economics of their AI features.

FAQ

Questions RAG Economics owners ask.

01
How does this MCP help with RAG costs?
It allows your AI client to calculate the total cost per query and find the most efficient chunking strategies to reduce token spend.
02
Can I use this to optimize my chunking strategy?
Yes. The optimize_chunking_strategy capability finds the ideal chunk size to balance retrieval accuracy against LLM token costs.
03
What clients are compatible with this MCP?
You can connect this MCP to any compatible client including Claude, Cursor, Windsurf, and VS Code.
04
Does this MCP help with latency issues?
Yes. You can use the analyze_latency_impact capability to estimate the additional infrastructure spend required to meet specific latency targets.
05
How is the data hosted?
Vinkius hosts and manages the MCP, so you don't have to worry about running it locally or managing credentials.
06
How does this capability help reduce RAG costs?
It identifies the primary cost drivers in your pipeline and suggests optimizations like adjusting chunk sizes or selecting more efficient retrieval strategies using get_optimization_priorities.
07
Can I model the cost of meeting specific latency SLAs?
Yes, the analyze_latency_impact capability calculates the estimated cost increase required to move from your current latency to a faster target latency.
08
What metrics are included in the cost breakdown?
The calculate_query_economics capability provides a detailed breakdown including embedding costs, retrieval costs, and LLM inference costs.

Explore

More in Analytics

Connect RAG Economics 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.

01-04

Calculate query economics

Get optimization priorities

Optimize chunking strategy

Analyze latency impact

What teams actually run

Scaling RAG Systems

Latency vs. Cost Tradeoffs

Parameter Tuning

Budget Forecasting

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

RAG Economics Connector

Claude Desktop

AI Engineers

MLOps Engineers

Product Managers

How does this MCP help with RAG costs?

Can I use this to optimize my chunking strategy?

What clients are compatible with this MCP?

Does this MCP help with latency issues?

How is the data hosted?

How does this capability help reduce RAG costs?

Can I model the cost of meeting specific latency SLAs?

What metrics are included in the cost breakdown?

AI SaaS Cost Analyzer AI Connector

AI Inference Cost Economics AI Connector

AI Memory Cost Analyzer AI Connector

Cost-Controlled Tool Selector AI Connector

Suggestions

AI MLOps Infrastructure Cost Analyzer AI Connector

RAG Implementation Cost Estimator AI Connector

Infrastructure Switching Cost Analysis AI Connector

rag-chunk-boundary-optimizer AI Connector