Making AI Coding Agents Production-Aware: Using Hud.io's MCP Server with Cloudflare Workers

Disclosure: This article is sponsored by Hud.io. I've been using Hud.io in production on FPL Hub and agreed to write this tutorial based on that experience. All technical examples and debugging scenarios are based on real usage.

Most AI coding tools excel during development but go blind once code hits production. When your edge functions start throwing errors at 2 AM, Cursor and Claude Desktop have no idea what's actually breaking in the real world. They can suggest fixes based on your code, but they're missing the critical context: runtime behavior, performance bottlenecks, and error patterns from actual user traffic.

This creates a frustrating gap. You're debugging production issues by manually checking logs, piecing together stack traces, and guessing at root causes—while your AI assistant sits idle, unable to help with the problems that matter most.

Hud.io's Model Context Protocol (MCP) server bridges this gap by giving AI agents direct access to production runtime data.

MCP (Model Context Protocol) is Anthropic's open standard for connecting AI agents to external data sources. Think of it as an API that AI agents can call to gather context they don't have natively. Hud.io exposes production runtime data through an MCP server, making that context available to any MCP-compatible AI tool.

In this tutorial, I'll show you how to integrate Hud.io with Cloudflare Workers, connect it to your IDE, and use AI agents to debug real production issues with full runtime context.

The Production Debugging Gap in Edge Environments

Edge computing platforms like Cloudflare Workers present unique debugging challenges. Your code runs across hundreds of data centers globally, making it difficult to understand exactly what's happening when things go wrong.

Traditional debugging approaches fall short:

Console logs scatter across regions. When a user in Tokyo hits an error, the logs live on an edge node you can't easily access. By the time you see aggregated logs, the context is gone.

Stack traces lack runtime context. You see that a function threw an error, but not the request patterns that led to it, the performance characteristics of surrounding code, or how often this failure actually occurs.

Performance issues hide in aggregate metrics. Your monitoring dashboard shows p95 latency spiking, but doesn't tell you which specific functions are slow, or what code paths are executing differently than usual.

I've experienced this firsthand with FPL Hub, my Fantasy Premier League platform that handles 500,000+ API calls daily across Cloudflare's edge network. A few months ago, we saw response times suddenly degrade for a subset of users. The aggregate metrics showed the problem, but identifying the specific code path taking 300ms instead of 30ms required hours of manual log analysis.

AI coding assistants can't help with these issues because they lack production awareness. They can review your code for potential bugs, but they can't tell you which functions are actually problematic in the real world.

Setting Up Hud.io in Cloudflare Workers

Hud.io works by instrumenting your code at the function level, collecting performance and error data without modifying your application logic. Hud.io uses function-level instrumentation rather than distributed tracing, which keeps overhead minimal while providing the runtime context AI agents need to debug effectively. The setup process is remarkably simple.

First, install the SDK in your Cloudflare Workers project:

npm install hud-sdk

Then add Hud.io to your Worker code. Here's a typical Cloudflare Worker with Hud.io integrated:

import Hud from 'hud-sdk';

// Initialize Hud with your API key
const hud = new Hud({
  apiKey: env.HUD_API_KEY,
  serviceName: 'fpl-hub-api'
});

export default {
  async fetch(request, env, ctx) {
    // Wrap your handler with Hud instrumentation
    return await hud.trace(async () => {
      const url = new URL(request.url);

      // Your application logic
      if (url.pathname === '/api/players') {
        return await handlePlayers(request, env);
      }

      if (url.pathname === '/api/fixtures') {
        return await handleFixtures(request, env);
      }

      return new Response('Not found', { status: 404 });
    });
  }
};

async function handlePlayers(request, env) {
  // Fetch player data from D1
  const players = await env.DB.prepare(
    'SELECT * FROM players WHERE active = 1'
  ).all();

  return Response.json(players);
}

async function handleFixtures(request, env) {
  // Fetch fixtures from external API
  const response = await fetch('https://fantasy.premierleague.com/api/fixtures/');
  const data = await response.json();

  return Response.json(data);
}

Store your Hud.io API key as a secret in your Worker:

wrangler secret put HUD_API_KEY

That's it. Hud.io automatically instruments your functions, tracking invocation counts, durations, exceptions, and performance characteristics without any additional configuration.

The SDK is designed for negligible overhead, even at high traffic. In production environments processing millions of requests, Hud.io's instrumentation adds minimal latency while providing comprehensive runtime visibility.

Connecting Hud's MCP Server to Your IDE

Once Hud.io is collecting production data, you need to expose that context to your AI coding tools. This is where the MCP server comes in.

Hud exposes runtime context via its MCP server, allowing AI agents in Cursor, Claude Desktop, or other MCP-compatible tools to query production behavior directly.

For Cursor users:

1. Install the Hud.io VS Code extension from the marketplace
2. Sign in using the command palette: Cmd+Shift+P → Hud: Sign In
3. Run Hud: Add MCP to configure the MCP server
4. Enable Hud in Cursor Settings → Tools & Integrations

For Claude Desktop users:

Run this command to register the MCP server:

claude mcp add hud --scope user
~/.hud/mcp start

What this enables:

Once configured, your AI agent can access production runtime data:

• Function-level performance metrics (invocation counts, durations, error rates)
• Recent error patterns and stack traces
• Performance trends over time
• Call graphs showing how functions interact in production

The key difference: instead of an AI agent reasoning about what might be wrong based on static code, it can see what is actually wrong based on real production behavior.

Real-World Debugging Scenario

Let me walk through a production debugging session that mirrors issues I've actually faced running FPL Hub. The specifics are simplified for clarity, but the debugging pattern is exactly how this works in practice.

The Problem:

You deploy a new feature to your Cloudflare Worker. Within an hour, you notice error rates climbing from 0.1% to 2.5%. Users are reporting intermittent failures when loading data.

Traditional debugging approach:

You'd spend the first 30 minutes just trying to reproduce the issue locally, often unsuccessfully because edge environments don't replicate perfectly. Then another hour correlating logs across regions to find the common pattern. You'd SSH into logs, grep for errors, manually trace through stack traces, and likely deploy additional logging just to understand what's happening.

With Hud.io + AI agents:

Open Cursor and ask your AI agent:

"What's causing the elevated error rate in production?"

The AI agent queries Hud's MCP server and responds:

"I'm seeing a spike in errors from the handlePlayers function. It's throwing 'TypeError: Cannot read property 'stats' of undefined' in 847 cases over the past hour. This started after deployment 3h2m ago. The error occurs when processing player data from the D1 database."

You ask for more context:

"Show me the specific code path that's failing"

The agent examines the call graph from production:

"The error occurs in this sequence:

1. handlePlayers queries D1 successfully
2. enrichPlayerData processes each player
3. calculateForm tries to access player.stats.lastFiveGames
4. For newly added players, stats object is null

The issue: your D1 migration added new player records without initializing the stats field."

Now you can see the problem. Here's the before and after:

// Before (causes errors)
async function calculateForm(player) {
  const recentGames = player.stats.lastFiveGames; // Breaks on null
  const points = recentGames.reduce((sum, game) => sum + game.points, 0);
  return { form: points / 5, trend: calculateTrend(recentGames) };
}

// After (handles edge case)
async function calculateForm(player) {
  // Add null check for newly added players
  if (!player.stats || !player.stats.lastFiveGames) {
    return { form: 0, trend: 'new' };
  }

  const recentGames = player.stats.lastFiveGames;
  const points = recentGames.reduce((sum, game) => sum + game.points, 0);
  return { form: points / 5, trend: calculateTrend(recentGames) };
}

Deploy the fix, and within minutes the agent confirms:

"Error rate is back to baseline. The calculateForm function is now handling null stats correctly across all edge locations."

What just happened:

Without leaving your IDE, you:

1. Identified the exact function causing errors
2. Understood the deployment that introduced the issue
3. Saw the specific code path failing in production
4. Applied a fix with confidence
5. Verified the fix worked across the global network

This debugging session took 10 minutes instead of hours, because the AI agent had direct access to production runtime context.

Performance Impact

One legitimate concern when adding instrumentation: overhead. Hud.io is designed for negligible overhead, even at high traffic.

In my experience running Hud.io on FPL Hub with 500,000+ daily API calls:

Latency impact: Unnoticeable. Response times at p50, p95, and p99 remained unchanged after enabling Hud.io.

CPU overhead: The SDK uses async telemetry collection with bounded buffers, so instrumentation doesn't block your application code.

Memory footprint: Minimal. Telemetry data is batched and sent to Hud's backend rather than stored in-memory.

The key is that Hud.io collects function-level aggregates rather than tracing every single request. It captures invocation counts, durations, and error rates without the overhead of distributed tracing or per-request sampling.

For edge environments where every millisecond matters, this design is critical. You get comprehensive runtime visibility without sacrificing the performance characteristics that make edge computing valuable.

What Hud.io Doesn't Do

To be clear about scope: Hud.io focuses on runtime observability at the function level, not distributed tracing or request-level debugging. If you need to trace a single request across multiple services with full span context, you'd want traditional APM tools with distributed tracing capabilities.

Hud.io's strength is function-level aggregates that give AI agents enough context to identify patterns without the overhead of per-request tracing. For edge environments where latency matters and debugging needs are typically about identifying problematic functions rather than tracing individual requests, this tradeoff makes sense.

For complex microservices architectures where request flows span many services, you might need both approaches—Hud.io for AI-powered debugging and traditional APM for detailed request tracing.

Conclusion

AI coding assistants are transforming software development, but they've been limited to helping with code that exists in your editor. Hud.io's MCP server changes that by giving AI agents direct access to production runtime behavior.

This closes the loop: your AI assistant can now help you debug the issues that actually matter—the ones affecting real users in production—with full context about function performance, error patterns, and code execution paths.

For Cloudflare Workers and other edge environments, this is especially valuable. The distributed nature of edge computing makes traditional debugging difficult, but Hud.io provides centralized visibility into runtime behavior across all edge locations.

The setup takes minutes, the overhead is negligible, and the debugging productivity gains are substantial. If you're building production applications with AI coding tools, bridging the gap between development and production context is worth the effort.

Get started with Hud.io at docs.hud.io. The SDK supports Node.js, Python, and other runtimes, with first-class support for serverless and edge environments.