MCP For Claude Marketplace
Builder Iteration Learning Rate

Builder Iteration Learning Rate MCP Connector for Claude

A+

Analyzes learning velocity and execution efficiency in iterative development cycles.

4 tools Official Updated Oct 1, 2026 Official Vinkius Partner
More Details Connect to Claude

This MCP server provides a specialized analytical engine to measure how effectively a builder converts iterative cycles into actionable knowledge and execution velocity. By utilizing tools like get_learning_rate_score and get_iteration_efficiency_audit, it calculates the efficiency of the learning loop, identifies improvement trajectories, and recommends an optimal iteration cadence. It helps identify if a process is accelerating, stalling, or churning based on implementation rates and pivot frequency.

learning-velocityiteration-optimizationefficiency-metricsbuilder-analyticsprocess-improvement

4 tools expose this connector's capabilities to your AI agent.

get_iteration_efficiency_audit

Provides a diagnostic overview by combining all metrics to identify if the builder is churning or stalling

get_optimal_cadence

Recommends the ideal frequency for future iterations to maximize efficiency

get_learning_rate_score

Calculates the primary efficiency metric for a builder's current iteration cycle

get_improvement_trajectory

Determines the momentum and direction of the builder's learning progress over time

See how to talk to your AI agent using Builder Iteration Learning Rate.

Calculate my current learning rate score with 10 iterations, 5 learnings per iteration, 80% implementation, and 2 pivots.

Your current learning rate score is 7.2, indicating a highly efficient and stable learning loop.

Check my efficiency status for 5 iterations, 2 learnings per iteration, 90% implementation, and 4 pivots.

Your efficiency status is 'Churning' due to a high pivot frequency relative to your implementation rate.

What is my improvement trajectory given these historical scores: 5.0, 5.5, 6.0?

Your trajectory shows an accelerating trend with a positive velocity, indicating increasing efficiency over time.

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

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