What kind of data does this MCP analyze?

It analyzes user interaction data within SaaS environments. Specifically, it tracks how often users accept, modify, or ignore AI-generated suggestions.

Is this for all AI products?

No. This MCP is designed specifically to evaluate the effectiveness and performance of AI-driven suggestions within a product's user flow.

Can I tell if the timing is good?

Yes. The get_contextual_efficiency_rating capability evaluates suggestion timing, telling you if the advice appeared at the optimal point relative to the user's actions.

Do I need to write code to use this?

No. You connect your AI client to the Vinkius catalog, and then you simply prompt your agent to run the specific analysis you need.

How is the quality score calculated?

The quality score combines acceptance and modification rates, giving higher weight to unmodified acceptances. It can also be adjusted based on whether suggestions were proactive.

Can I see which suggestion types are most effective?

Yes, you can use get_type_performance_breakdown to see the specific acceptance rates for each category of suggestion.

What does the efficiency rating measure?

The efficiency rating measures how well the timing of a suggestion aligns with user actions using get_contextual_efficiency_rating.

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Connect AI Suggestion Effectiveness Analyzer 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. Pinpoint exactly where your AI features succeed or fail.

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 Suggestion Analyzer.

01
Get suggestion acceptance metrics
Calculates raw performance metrics for AI suggestions
02
Get type performance breakdown
03
Get contextual efficiency rating
Evaluates the efficiency of suggestion timing relative to user action
04
Get quality score report
Calculates the overall quality score and status of AI suggestions

Real work

What teams actually run

Post-Feature Launch Review

After rolling out a new suggestion type, you run the raw performance metrics to see if users are accepting it at the expected rate.

Optimizing User Flow

You check the contextual efficiency rating to determine if the suggestion appears right after the user completes a specific action.

AI Model Tuning

You use the type performance breakdown to see if the model needs to be retrained for certain suggestion categories.

Stakeholder Reporting

You generate a quality score report to give executives a simple, high-level view of AI feature health.

One connector, every AI

AI Suggestion Analyzer 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 Suggestion Analyzer 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

856ms average. Fast in production.

AI Suggestion Analyzer is checked daily against the live service.

Daily averagePeak 1159ms

Oct 3Today

Fastest day: 706ms
Slowest day: 1159ms
14-day trend: Slowing+29%

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 Suggestion Analyzer, so you can see the experience inside your AI.

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

AI Suggestion Analyzer Connector

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

Connector linkhttps://edge.vinkius.com/vk_preview_xsHdpP3NaeYyySTS8NjlH6HtQFq1f3SHn9JHlq6R/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 Suggestion Analyzer capabilities are ready to use.

Configuration · claude_desktop_config.jsonCopy

{
  "mcpServers": {
    "ai-suggestion-effectiveness-analyzer-mcp": {
      "url": "https://edge.vinkius.com/vk_preview_xsHdpP3NaeYyySTS8NjlH6HtQFq1f3SHn9JHlq6R/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 Suggestion Analyzer in Claude and other AIs

See all the AI clients this connector works with ↑

Who it's for

Built for the work AI Suggestion Analyzer owners hand off.

Product Managers, UX Researchers, and ML Engineers use this MCP. If you're responsible for the success of AI features in a SaaS product, you need this. It gives you the hard data to justify your roadmap decisions.

01
Product Manager
You use it to prove the ROI of new AI features and prioritize development efforts.
02
UX Researcher
You use it to validate user interaction patterns and adjust suggestion placement.
03
ML Engineer
You use it to tune the suggestion models, focusing on improving acceptance and modification rates.

FAQ

Questions AI Suggestion Analyzer owners ask.

01
What kind of data does this MCP analyze?
It analyzes user interaction data within SaaS environments. Specifically, it tracks how often users accept, modify, or ignore AI-generated suggestions.
02
Is this for all AI products?
No. This MCP is designed specifically to evaluate the effectiveness and performance of AI-driven suggestions within a product's user flow.
03
Can I tell if the timing is good?
Yes. The get_contextual_efficiency_rating capability evaluates suggestion timing, telling you if the advice appeared at the optimal point relative to the user's actions.
04
Do I need to write code to use this?
No. You connect your AI client to the Vinkius catalog, and then you simply prompt your agent to run the specific analysis you need.
05
How is the quality score calculated?
The quality score combines acceptance and modification rates, giving higher weight to unmodified acceptances. It can also be adjusted based on whether suggestions were proactive.
06
Can I see which suggestion types are most effective?
Yes, you can use get_type_performance_breakdown to see the specific acceptance rates for each category of suggestion.
07
What does the efficiency rating measure?
The efficiency rating measures how well the timing of a suggestion aligns with user actions using get_contextual_efficiency_rating.

Explore

More in Productivity

Connect AI Suggestion Analyzer 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

Get suggestion acceptance metrics

Get type performance breakdown

Get contextual efficiency rating

Get quality score report

What teams actually run

Post-Feature Launch Review

Optimizing User Flow

AI Model Tuning

Stakeholder Reporting

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

AI Suggestion Analyzer Connector

Claude Desktop

Product Manager

UX Researcher

ML Engineer

What kind of data does this MCP analyze?

Is this for all AI products?

Can I tell if the timing is good?

Do I need to write code to use this?

How is the quality score calculated?

Can I see which suggestion types are most effective?

What does the efficiency rating measure?

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