Does this MCP work with all LLMs and SLMs?

It is designed to optimize model portfolios generally. You provide the necessary capabilities data, and the MCP calculates the optimal mix based on those inputs.

What kind of data do I need to provide?

You need to provide detailed information about your workload, including the percentage distribution of different task types and the required accuracy thresholds for those tasks.

Is this just a theoretical cost estimate?

No, the MCP provides concrete, quantifiable reports. You can get a cost savings report that directly compares your optimized setup against a pure LLM baseline.

Can I use this if I don't know my performance requirements?

The MCP helps you define those requirements. You can use the tradeoff matrix to visualize the impact of changing performance targets on your overall cost.

How does the optimizer account for routing errors?

The calculate_optimal_mix capability uses the routingAccuracy parameter to adjust expected performance, ensuring that even if a query is misrouted to a less capable model, the overall performance remains above your defined threshold.

Can I compare my current setup to an optimized one?

Yes, you can use get_cost_savings_report to compare the cost of your optimized portfolio against a baseline where only LLMs are used for every task.

How do I know if my model mix is actually viable?

You can use validate_portfolio_feasibility to check if a specific proposed allocation of LLMs and SLMs will meet your required performance levels given your current routing accuracy.

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Connect AI Portfolio Economics Optimizer 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. Cut operational costs without sacrificing performance.

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 · 4 capabilities

We ship only the actions that matter.

Capability set01 / 01

01-04

4 capabilities in this set.

Part of 4 available through AI Portfolio Economics.

01
Calculate optimal mix
Note: capabilities are required for the calculation. Determines the best ratio of LLMs to SLMs to minimize cost while meeting performance requirements
02
Generate tradeoff matrix
Visualizes the relationship between cost reduction and performance degradation
03
Get cost savings report
Compares optimized portfolio cost against a pure LLM baseline
04
Validate portfolio feasibility
Validates if a proposed LLM/SLM mix meets performance requirements

Real work

What teams actually run

High-Volume Summarization Pipeline

You process thousands of documents daily. The MCP determines if SLMs can handle the bulk of summarization tasks, reserving LLMs only for complex, high-value documents.

Chatbot Deployment

Your chatbot needs to handle both simple FAQs and complex reasoning. You use the MCP to find the perfect mix that keeps the chat affordable while maintaining high accuracy.

Data Classification System

You classify incoming data into categories. The MCP helps you assign the right model (LLM or SLM) based on the complexity of the classification task to save money.

Internal Knowledge Base Search

You need to search a large, complex knowledge base. The MCP optimizes the model mix to ensure search relevance remains high while minimizing query costs.

One connector, every AI

AI Portfolio 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 AI Portfolio 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

932ms average. Fast in production.

AI Portfolio Economics is checked daily against the live service.

Daily averagePeak 1377ms

Oct 3Today

Fastest day: 710ms
Slowest day: 1377ms
14-day trend: Slowing+37%

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 AI Portfolio Economics, so you can see the experience inside your AI.

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

AI Portfolio Economics Connector

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

Connector linkhttps://edge.vinkius.com/vk_preview_XzAEDVOVYaCSnEL7ACyI8wrokOIjK3bIH83SxaU0/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 — AI Portfolio Economics capabilities are ready to use.

Configuration · claude_desktop_config.jsonCopy

{
  "mcpServers": {
    "ai-portfolio-economics-optimizer-mcp": {
      "url": "https://edge.vinkius.com/vk_preview_XzAEDVOVYaCSnEL7ACyI8wrokOIjK3bIH83SxaU0/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 AI Portfolio Economics in Claude and other AIs

See all the AI clients this connector works with ↑

Who it's for

Built for the work AI Portfolio Economics owners hand off.

This MCP is essential for AI Architects and ML Engineers who are responsible for operational budgets. If you're building complex AI pipelines, you need to prove that your model choices are both accurate and affordable. It lets you move beyond simple proof-of-concept models into production-ready, cost-optimized systems.

01
ML Architect
Designs the overall model stack and uses this MCP to balance performance requirements against budget constraints.
02
AI Developer
Integrates the optimal model mix into the application code, ensuring the final product is efficient and cheap to run.
03
Technical Product Manager
Uses the cost savings reports to justify architectural changes and budget allocations to stakeholders.

FAQ

Questions AI Portfolio Economics owners ask.

01
Does this MCP work with all LLMs and SLMs?
It is designed to optimize model portfolios generally. You provide the necessary capabilities data, and the MCP calculates the optimal mix based on those inputs.
02
What kind of data do I need to provide?
You need to provide detailed information about your workload, including the percentage distribution of different task types and the required accuracy thresholds for those tasks.
03
Is this just a theoretical cost estimate?
No, the MCP provides concrete, quantifiable reports. You can get a cost savings report that directly compares your optimized setup against a pure LLM baseline.
04
Can I use this if I don't know my performance requirements?
The MCP helps you define those requirements. You can use the tradeoff matrix to visualize the impact of changing performance targets on your overall cost.
05
How does the optimizer account for routing errors?
The calculate_optimal_mix capability uses the routingAccuracy parameter to adjust expected performance, ensuring that even if a query is misrouted to a less capable model, the overall performance remains above your defined threshold.
06
Can I compare my current setup to an optimized one?
Yes, you can use get_cost_savings_report to compare the cost of your optimized portfolio against a baseline where only LLMs are used for every task.
07
How do I know if my model mix is actually viable?
You can use validate_portfolio_feasibility to check if a specific proposed allocation of LLMs and SLMs will meet your required performance levels given your current routing accuracy.

Explore

More in Optimization

Connect AI Portfolio 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 optimal mix

Generate tradeoff matrix

Get cost savings report

Validate portfolio feasibility

What teams actually run

High-Volume Summarization Pipeline

Chatbot Deployment

Data Classification System

Internal Knowledge Base Search

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

AI Portfolio Economics Connector

Claude Desktop

ML Architect

AI Developer

Technical Product Manager

Does this MCP work with all LLMs and SLMs?

What kind of data do I need to provide?

Is this just a theoretical cost estimate?

Can I use this if I don't know my performance requirements?

How does the optimizer account for routing errors?

Can I compare my current setup to an optimized one?

How do I know if my model mix is actually viable?

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