MCP Eval Pro - Production Readiness Assessment for MCP Servers

Evaluate. Optimize. Deploy with Confidence. MCP Eval Pro is the first comprehensive evaluation framework designed specifically for Model Context Protocol (MCP) servers, helping you transform experimental AI integrations into battle-tested production systems.

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🌟 Why MCP Eval Pro?

Think of MCP servers as the nervous system of your AI application. Just as a doctor wouldn't prescribe medication without running tests, you shouldn't deploy MCP servers without rigorous evaluation. MCP Eval Pro acts as your diagnostic toolkit, measuring everything from response quality to token costs, ensuring your AI infrastructure is healthy, efficient, and ready for prime time.

The Problem: MCP servers are powerful but unpredictable. Without proper testing, you risk high latency, ballooning API costs, and inconsistent AI behavior that frustrates users.

The Solution: MCP Eval Pro provides automated, repeatable testing with visual reports that make optimization decisions obvious. Stop guessing and start measuring.

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🔧 Core Architecture

graph TD
    A[Your MCP Server] --> B[MCP Eval Pro Engine]
    B --> C{Evaluation Pipeline}
    C --> D[Tool Testing Module]
    C --> E[Token Cost Analyzer]
    C --> F[Response Quality Grader]
    C --> G[Latency Profiler]
    D --> H[Test Results Database]
    E --> H
    F --> H
    G --> H
    H --> I[Visual Report Generator]
    I --> J[HTML Dashboard]
    I --> K[PDF Summary]
    I --> L[CLI Output]
    H --> M[Production Readiness Score]
    M --> N[Pass/Fail Criteria]

The evaluation engine processes MCP server endpoints through four parallel assessment modules, aggregating results into a comprehensive readiness score with human-readable visualizations.

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🚀 Getting Started

Prerequisites

• Python 3.9+ or Node.js 18+
• An MCP server (local or remote)
• API keys for evaluation (OpenAI or Claude)

Installation

# Using pip
pip install mcp-eval-pro

# Using npm
npm install -g mcp-eval-pro

Example Profile Configuration

Create an mcp-eval-profile.yaml file:

server:
  endpoint: "http://localhost:8000/mcp"
  protocol_version: "2024-01"
  
evaluation:
  test_suite: "production"  # Options: basic, standard, production
  iterations: 5             # Number of test runs per tool
  timeout_ms: 30000         # Maximum wait time per request
  
metrics:
  token_tracking: true      # Track input/output token usage
  cost_calculation: true    
  response_quality: true    # Grade responses with AI grader
  latency_profile: true     # Millisecond-precision timing
  
reporting:
  format: "html"            # html, pdf, json, or cli
  output_dir: "./eval_reports"
  include_charts: true
  customer_friendly: true   # Hide technical details
  
slack_webhook: ""           # Optional: post results to Slack

Example Console Invocation

# Basic evaluation
mcp-eval --profile mcp-eval-profile.yaml

# Quick check with CLI output
mcp-eval --quick --endpoint http://localhost:8000/mcp

# Full production assessment with visual report
mcp-eval --production --output ./reports --format html

# Compare two MCP servers side-by-side
mcp-eval --compare server1.yaml server2.yaml

# Continuous monitoring mode
mcp-eval --watch --interval 300  # Check every 5 minutes

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📊 Key Features

1. Multi-Model AI Grading 🤖

Leverage both OpenAI GPT-4 and Claude 3 Opus to grade response quality. The AI evaluator checks for:

• Contextual relevance
• Factual accuracy
• Response completeness
• Safety and bias checks
• Format compliance

2. Token Cost Intelligence 💰

Track every token your MCP server consumes across multiple runs. The cost analyzer provides:

• Per-tool cost breakdowns
• Estimated monthly costs at diff