🔬 Best Practices for Benchmarking MCP Servers

[greynewell]

Let's compile community best practices for getting reliable, meaningful benchmark results!

📏 Sample Size

Question: How many tasks should I run for reliable results?

Share your experience:

• What sample sizes have you used?
• When did results stabilize?
• What's the sweet spot for cost vs. confidence?

🎯 Configuration Tips

Question: What configurations have worked well for you?

Topics:

• Model selection (Opus vs Sonnet vs Haiku)
• Timeout settings
• Concurrency levels
• MCP server configurations
• Prompt engineering

📊 Result Interpretation

Question: How do you interpret and present results?

Share your approaches:

• What metrics do you focus on?
• How do you handle variance?
• What makes a "good" improvement?
• How do you identify what's working?

🐛 Debugging Failed Runs

Question: What strategies help when tasks fail?

Tips for:

• Using --log-dir effectively
• Identifying patterns in failures
• Optimizing MCP server based on errors
• Docker troubleshooting

💰 Cost Optimization

Question: How do you minimize API costs while getting good data?

Strategies:

• Which model to use when?
• Sample size strategies
• Running in stages
• Using cached results

⚡ Performance Optimization

Question: How do you speed up benchmark runs?

Share tips on:

• Optimal concurrency settings
• Docker optimization
• Image caching
• Hardware considerations

🔬 Comparing MCP Servers

Question: How do you fairly compare different MCP servers?

Best practices for:

• Controlling variables
• Running multiple iterations
• Accounting for randomness
• Presenting comparative results

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Share your knowledge! What have you learned from running benchmarks? What mistakes did you make that others can avoid?