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ClaudeChatGPTPerplexityGeminiMicrosoft CopilotRaycastMeta AIGrokZ.aiQwenKimi
DeepSeekMistralCursorVS CodeWindsurfJetBrainsClineLovableVercel AI SDKLangChain

Use AI Suggestion Analyzer with your AI.

Connect your account once and let the AI you already use work with it, without building another integration. Pinpoint exactly where your AI features succeed or fail.

Included with plan

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.

ChatGPTClaudeCursorPerplexityGeminiMicrosoft CopilotRaycastMeta AI

Complete set · 4 capabilities

The complete AI Suggestion Analyzer capability set.

These are the exact actions your AI can choose when you ask it to work with AI Suggestion Analyzer.

Capability set01 / 01

01-04

4 capabilities in this set.

Part of 4 available through AI Suggestion Analyzer.

  1. 01

    Get contextual efficiency rating

    Evaluates the efficiency of suggestion timing relative to user action

  2. 02

    Get quality score report

    Calculates the overall quality score and status of AI suggestions

  3. 03

    Get suggestion acceptance metrics

    Calculates raw performance metrics for AI suggestions

  4. 04

    Get type performance breakdown

Observed, not estimated

795ms average. Fast in production.

AI Suggestion Analyzer is checked daily against the live service.

Daily averagePeak 979ms
Aug 28Today
Fastest day
746ms
Slowest day
979ms
14-day trend
Slowing+5%

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.

  1. 1In Claude Desktop, open Settings → Connectors.
  2. 2Click “Add custom connector” and paste the connector link above as the remote MCP server URL.
  3. 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

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.

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