mcpbr

🥈Silver

Evaluate MCP servers using Model Context Protocol Benchmark Runner. Operations teams benchmark server performance against real GitHub issues. Connects to MCP servers and GitHub, providing quantifiable metrics for optimization.

2090Updated 1w ago

Intermediate30min to implementautomation

Saves ~120 min per use

Quick InstallView Source

git clone https://github.com/greynewell/mcpbr.git

Works with:

Claude

Overview

About This Skill

Setup & Installation

Quick Install

No install command available. Check the GitHub repository for manual installation instructions.

Alternative Install (Git Clone)

git clone https://github.com/greynewell/mcpbr

Requirements

Claude Code or compatible AI agent
Works with: Claude

Quick Start Guide

Install the Skill

Copy the install command above and run it in your terminal.

Open Your AI Agent

Launch Claude Code, Cursor, or your preferred AI coding agent.

Try It Out

Use the prompt template or examples below to test the skill.

Customize

Adapt the skill to your specific use case and workflow.

Usage Examples

Prompt Template

Evaluate the MCP server for [COMPANY] in the [INDUSTRY] sector using the Model Context Protocol Benchmark Runner. Analyze performance against [NUMBER] recent GitHub issues related to [SPECIFIC_TOPIC]. Provide quantifiable metrics for optimization, including response time, accuracy, and resource utilization.

Example Output

# MCP Server Performance Evaluation

## Overview
The MCP server for TechCorp in the software development industry was evaluated using the Model Context Protocol Benchmark Runner. The analysis focused on 50 recent GitHub issues related to API integration.

## Key Metrics
- **Response Time**: Average response time was 1.2 seconds, with a standard deviation of 0.3 seconds.
- **Accuracy**: The server achieved an accuracy rate of 92% in resolving issues.
- **Resource Utilization**: CPU usage peaked at 75% during high-load periods, while memory usage remained stable at 60%.

## Recommendations
- **Optimization**: Implement caching mechanisms to reduce response time during peak loads.
- **Scalability**: Consider horizontal scaling to distribute the load more evenly.
- **Monitoring**: Set up continuous monitoring to track performance metrics in real-time.