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Connect Builder Iteration Learning Rate 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. Track how fast your development cycles actually turn into progress.

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 Builder Iteration Learning Rate.

  1. 01

    Get iteration efficiency audit

    This capability provides a diagnostic overview of your entire process. It combines all your metrics to show if you are stalling or just churning through tasks.

  2. 02

    Get optimal cadence

    This capability calculates your ideal iteration frequency. It recommends how often you should run cycles to maximize your efficiency.

  3. 03

    Get learning rate score

    This capability generates your primary efficiency metric. It calculates a specific score for your current iteration cycle.

  4. 04

    Get improvement trajectory

    This capability tracks your momentum over time. It determines the direction and speed of your learning progress across multiple cycles.

Real work

What teams actually run

01

Rapid Prototyping

Check if your quick experimental loops are actually resulting in usable features or just wasted effort.

02

Process Optimization

Find the sweet spot for how often you should pivot or iterate to keep momentum high.

03

Performance Monitoring

Monitor your learning trajectory to see if your development efficiency is improving over weeks.

04

Pivot Analysis

Determine if your frequent changes are helping you learn or just causing you to churn.

One connector, every AI

Builder Iteration Learning Rate 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 Builder Iteration Learning Rate 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

897ms average. Fast in production.

Builder Iteration Learning Rate is checked daily against the live service.

Daily averagePeak 1066ms
Oct 14Today
Fastest day
870ms
Slowest day
1066ms
14-day trend
Slowing+19%

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 Builder Iteration Learning Rate, so you can see the experience inside your AI.

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

Builder Iteration Learning Rate Connector

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

Connector linkhttps://edge.vinkius.com/vk_preview_e3VeUOBAKvMDcz7FVGFdPWuXXo6UnIPfMk2DGp2s/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 — Builder Iteration Learning Rate capabilities are ready to use.
Configuration · claude_desktop_config.jsonCopy
{
  "mcpServers": {
    "builder-iteration-learning-rate-mcp": {
      "url": "https://edge.vinkius.com/vk_preview_e3VeUOBAKvMDcz7FVGFdPWuXXo6UnIPfMk2DGp2s/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 Builder Iteration Learning Rate in Claude and other AIs

See all the AI clients this connector works with ↑

Who it's for

Built for the work Builder Iteration Learning Rate owners hand off.

This MCP is built for technical builders who need to quantify their development speed and process health.

  • 01

    Software Engineers

    They hand off iteration counts and implementation rates to the agent to check for burnout or stalling.

  • 02

    Product Managers

    They use the data to determine if the team is moving fast enough or pivoting too often.

  • 03

    Solo Founders

    They use the metrics to ensure their rapid prototyping cycles are actually yielding progress.

FAQ

Questions Builder Iteration Learning Rate owners ask.

  • 01

    What does a 'churning' status mean?

    Churning happens when your pivot frequency is too high compared to your implementation rate, meaning you are changing direction more than you are making progress.

  • 02

    How do I use this with Claude or Cursor?

    You connect your client to the Vinkius platform once, and then your agent can use these capabilities directly within your existing workflow.

  • 03

    Can this MCP help me decide how often to iterate?

    Yes, the get_optimal_cadence capability specifically recommends the ideal frequency for your future iterations to maximize efficiency.

  • 04

    What metrics are needed to calculate a score?

    You typically provide the number of iterations, learnings per iteration, implementation percentage, and the number of pivots.

  • 05

    Does this track my progress over time?

    Yes, the get_improvement_trajectory capability analyzes your historical scores to show your momentum and direction.

  • 06

    What is the Learning Rate Score?

    The Learning Rate Score is a normalized metric that represents the efficiency of your iteration loop by combining knowledge gain with implementation speed.

  • 07

    How can I identify if my process is stalling?

    You can use the get_iteration_efficiency_audit capability. Stalling is typically identified when the implementation rate is high but the number of learnings per iteration is near zero.

  • 08

    What does the optimal cadence recommendation mean?

    The get_optimal_cadence capability suggests the ideal frequency for future cycles. For example, if pivot frequency is high, it may suggest a longer interval to ensure stability.

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