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Connect Cohort Retention Analytics 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. Turn raw retention data into clear product health metrics.

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

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.

A futuristic MCP client docking bay
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 Cohort Retention Analytics.

  1. 01

    Calculate average lifetime

    This capability determines the expected number of months a user stays active based on your data.

  2. 02

    Calculate retention curve

    Use this to generate a sequence of data points that show how a cohort decays over time.

  3. 03

    Evaluate retention milestone

    This capability checks the specific retention percentage for a user at a requested point in time.

  4. 04

    Compare performance to benchmark

    This capability compares your specific metrics against hardcoded industry standards for categories like SaaS or Gaming.

Real work

What teams actually run

01

Measuring Product Stickiness

Your agent builds a retention curve from monthly data to show how quickly users leave.

02

Benchmarking Growth

You compare your current E-Commerce retention against industry standards to see if you are at risk.

03

Predicting LTV

The AI uses lifetime calculations to help you understand the long-term value of your cohorts.

04

Milestone Tracking

You check if your cohort hit a specific retention target at the three-month mark.

Automate a task

Put Cohort Retention Analytics to work on one thing.

Step-by-step automations your AI runs through this Connector: the workflow, the prompts to start with, and the exact capabilities behind each move.

One connector, every AI

Cohort Retention Analytics 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.

ClaudeClaudeChatGPTChatGPTGeminiGeminiPerplexityPerplexityGrokGrokMicrosoft CopilotMicrosoft CopilotCursorCursorVS CodeVS CodeWindsurfWindsurfJetBrainsJetBrainsClineClineLangChainLangChainVercel AI SDKVercel AI SDKLovableLovableZ.aiZ.aiRaycastRaycastQwen CodeQwen CodeKimi CodeKimi CodeLe ChatLe Chat

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

You don't need a client to put Cohort Retention Analytics 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

696ms average. Fast in production.

Cohort Retention Analytics is checked daily against the live service.

Daily averagePeak 850ms
Oct 3Today
Fastest day
518ms
Slowest day
850ms
14-day trend
Slowing+13%

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 Cohort Retention Analytics, so you can see the experience inside your AI.

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

Cohort Retention Analytics Connector

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

Connector linkhttps://edge.vinkius.com/vk_preview_0clZooXkip3nj1SVREyeX8jIZAq7iA6bQySCHT9q/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 — Cohort Retention Analytics capabilities are ready to use.
Configuration · claude_desktop_config.jsonCopy
{
  "mcpServers": {
    "cohort-retention-analytics-mcp": {
      "url": "https://edge.vinkius.com/vk_preview_0clZooXkip3nj1SVREyeX8jIZAq7iA6bQySCHT9q/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 Cohort Retention Analytics in Claude and other AIs

See all the AI clients this connector works with ↑

Who it's for

Built for the work Cohort Retention Analytics owners hand off.

Product teams use this to monitor user stickiness and long-term growth health.

  • 01

    Product Managers

    They hand off raw cohort arrays to the AI to get instant health reports.

  • 02

    Data Analysts

    They use the MCP to automate repetitive cohort math and benchmark comparisons.

  • 03

    Growth Leads

    They use the capability to see if new features are improving long-term user retention.

FAQ

Questions Cohort Retention Analytics owners ask.

  • 01

    What industries is this MCP benchmarked for?

    The MCP includes hardcoded benchmarks for SaaS, E-Commerce, Gaming, and FinTech.

  • 02

    Which AI clients can I use with this MCP?

    You can use this with any MCP-compatible client, including Claude, Cursor, Windsurf, and VS Code.

  • 03

    Do I need to host this myself?

    No, Vinkius hosts and manages the MCP for you. You just connect it once and it is ready to use.

  • 04

    Can I use this to calculate user lifetime?

    Yes, the calculate_average_lifetime capability determines the expected number of months a user stays active.

  • 05

    How do I compare my data to industry standards?

    Use the compare_performance_to_benchmark capability to check your metrics against specific category standards.

  • 06

    How do I calculate the retention curve?

    Use the calculate_retention_curve capability by providing an array of retention rates where the first element is 1.0 (representing 100% retention at Month 0).

  • 07

    How can I check if my SaaS retention is good?

    Use the compare_performance_to_benchmark capability. Pass 'SaaS' as the product category and your observed metric as the actual value to see if it is 'Above Benchmark' or 'At Risk'.

  • 08

    What does average lifetime represent in this capability?

    The calculate_average_lifetime capability calculates the expected number of months a user remains active by summing all retention rates provided in your input array.

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