Understanding MCP Tracking

Mean Cumulative Precision (MCP) tracking is essential for understanding how well your content performs in LLM responses. It helps measure both the frequency and position of your content citations, providing valuable insights into your content's effectiveness.

Implementation Options

There are two main ways to implement MCP tracking:

  1. Using our free MCP Score Tracker tool for basic tracking
  2. Setting up your own MCP tracking server using the MCP boilerplate

Option 1: Using the MCP Score Tracker

Our free MCP Score Tracker tool provides:

  • Real-time MCP score calculation
  • Citation position tracking
  • Prompt coverage analysis
  • Historical trend visualization

To use the tracker:

  1. Enter your content URL
  2. Add test prompts that are relevant to your content
  3. Select your desired tracking period
  4. Click "Calculate MCP Score" to get your results

Option 2: Custom MCP Server Implementation

For more advanced tracking needs, you can set up your own MCP tracking server using the MCP Boilerplate. This provides:

  • Complete control over tracking implementation
  • User authentication
  • Optional paid tools integration
  • Custom metrics and analytics

Setting Up Your Own MCP Server

Prerequisites

  • Node.js installed on your system
  • A Cloudflare account
  • Basic understanding of TypeScript
  • (Optional) Stripe account for paid features

Step-by-Step Implementation


# 1. Clone the boilerplate
git clone https://github.com/iannuttall/mcp-boilerplate
cd mcp-boilerplate

# 2. Install dependencies
npm install

# 3. Set up environment variables
cp .dev.vars.example .dev.vars
# Edit .dev.vars with your configuration

Code Implementation

Here's an example of implementing a simple MCP tracking tool:


// src/tools/mcpTracker.ts
import { z } from "zod";
import { PaidMcpAgent } from "@stripe/agent-toolkit/cloudflare";

export function mcpTracker(
  agent: PaidMcpAgent,
  env?: { BASE_URL: string }
) {
  agent.tool(
    "track_mcp_score",
    {
      url: z.string().url(),
      prompts: z.array(z.string()),
      timeframe: z.number()
    },
    async ({ url, prompts, timeframe }) => {
      // Implement your MCP tracking logic here
      const mcpScore = await calculateMcpScore(url, prompts, timeframe);
      
      return {
        content: [
          { 
            type: "text", 
            text: `MCP Score: ${mcpScore.value}\n` +
                  `Citations: ${mcpScore.citations}\n` +
                  `Average Position: ${mcpScore.avgPosition}`
          }
        ]
      };
    }
  );
}

Best Practices for MCP Tracking

  • Track multiple URLs to compare performance
  • Use diverse test prompts for better coverage
  • Monitor trends over time, not just single scores
  • Set up alerts for significant score changes
  • Regularly update your test prompts to match current search patterns

Interpreting MCP Results

Your MCP score typically ranges from 0 to 1, where:

  • 0.8 - 1.0: Excellent citation performance
  • 0.6 - 0.8: Good performance
  • 0.4 - 0.6: Average performance
  • Below 0.4: Needs improvement

Next Steps

After setting up your MCP tracking system:

  1. Regularly monitor your scores
  2. Analyze patterns in high-performing content
  3. Optimize content based on tracking insights
  4. Consider implementing A/B testing