MCP Integration: Claude Desktop Gets Production Context
Summary
The Model Context Protocol (MCP) is Anthropic's open standard that lets AI assistants access external data sources. Cazon's MCP server connects Claude Desktop to your production errors, enriched with pattern matching and impact scores. Instead of copying stack traces into chat, Claude can query your error database directly, understand which errors matter most, and suggest fixes based on your team's patterns. One config file change, and your AI assistant becomes production-aware.
AI coding assistants are brilliant at fixing bugs when you give them all the context. The problem is gathering that context: copying error messages from monitoring tools, finding stack traces in logs, explaining which errors matter most, describing patterns you've seen before. By the time you've assembled everything Claude needs, you could have debugged the issue yourself.
This is the gap that MCP bridges. Instead of you playing messenger between production and AI, the AI queries production directly.
What Is MCP?
The Model Context Protocol is an open standard from Anthropic that defines how AI assistants connect to external data sources. Think of it as a universal API specifically designed for AI context.
Before MCP, every AI assistant needed custom integrations for each tool: Copilot had GitHub integration, Cursor had filesystem integration, Continue.dev had documentation integration. Each one reinvented the wheel. MCP standardizes this: build one MCP server, and it works with any MCP-compatible AI assistant.
For developers, this means:
- One integration, multiple AIs: Build once for Claude, Copilot, Cursor, Continue.dev
- Standardized protocol: No proprietary APIs or authentication schemes
- Resource and tool pattern: AIs can read context (resources) and take actions (tools)
- Local-first: MCP servers run on your machine with your credentials
Anthropic designed MCP specifically for context-heavy workflows where AI assistants need access to external systems: databases, monitoring tools, documentation, ticketing systems. Cazon's error intelligence fits perfectly into this model.
How Cazon's MCP Server Works
The Cazon MCP server is a lightweight process that runs on your machine, connects to Cazon's API using your key, and exposes error intelligence to AI assistants through MCP's standard protocol.
Installation is simple:
npm install -g @cazon/mcp-server
Then add one config block to Claude Desktop:
{
"mcpServers": {
"cazon": {
"command": "cazon-mcp",
"env": {
"CAZON_API_KEY": "your-api-key",
"CAZON_BASE_URL": "https://cazon.dev"
}
}
}
}
Restart Claude Desktop, and it immediately has access to your production errors.
What Claude Can Do with Production Context
Once connected, Claude Desktop gains four new capabilities through Cazon:
1. Intelligent Triage
Instead of asking "what should I fix first?", you can ask Claude "what are my critical errors?" The MCP server calls cazon_triage_errors, which returns errors categorized by impact score with occurrence counts, trends, and affected users. Claude sees exactly what matters most without you copying data.
2. Error Analysis
When debugging, Claude can call cazon_analyze_error to get pattern-matched suggestions. If the error matches one of our 353 known patterns, Claude gets the fix instantly. If not, the API uses AI fallback to generate a suggestion, which Claude then incorporates into its response. This means Claude benefits from both our pattern library and its own reasoning.
3. Pattern Discovery
Claude can call cazon_get_patterns to see common error patterns across your codebase: which errors happen most frequently, which are trending up, which affect the most users. This gives Claude the same view your engineering manager would have when prioritizing work.
4. Similar Error Search
When you're debugging something unfamiliar, Claude can call cazon_find_similar to search your error history for related issues. If your team fixed this error before, Claude learns from that context without you remembering to mention it.
The key insight is that Claude doesn't just fetch data; it interprets error intelligence the same way a senior developer would. Impact scores become "this is critical, I should fix it now." Trends become "this is getting worse, might be a recent regression." Patterns become "I've seen this before, here's what worked."
Example Conversation
Watch Cazon triage errors in Claude Desktop
Here's what debugging looks like with MCP-powered Claude:
You: "What errors should I focus on today?"
Claude: Let me check your production errors...
[Calls cazon_triage_errors]
You have 3 critical errors I recommend addressing:
1. TypeError in payment checkout (Impact: 87/100)
- 45 occurrences, affecting 15 users
- Trend: 📈 Increasing (potential regression)
- Location: checkout.js:156
2. Database timeout in user service (Impact: 82/100)
- 67 occurrences, affecting 28 users
- Trend: ➡️ Stable
- Location: userService.js:89
The payment checkout error is increasing, which suggests a recent change introduced this. Should I analyze it?
You: "Yes, analyze the payment error"
Claude: [Calls cazon_analyze_error with error details]
This is a known pattern: accessing array properties without null checking. The error occurs when cart.items is undefined, likely when users navigate directly to checkout without adding items.
Here's a fix:
// Before
const total = cart.items.reduce((sum, item) => sum + item.price, 0);
// After
const total = cart.items?.reduce((sum, item) => sum + item.price, 0) ?? 0;
This pattern affects 15 users, so it's high priority. The fix is straightforward; optional chaining prevents the TypeError. Want me to check if we've seen similar issues elsewhere?
Notice what didn't happen: You didn't copy stack traces, you didn't explain impact scores, you didn't describe the error pattern. Claude queried Cazon directly and got enriched context automatically.
Why MCP Matters for Error Intelligence
Cazon's error intelligence is most valuable when it's available exactly where developers work. That's why we've built three integration points:
VS Code Extension - Inline errors in your editor with impact scores and suggestions MCP Server - Production context in Claude Desktop (and soon, other AI assistants) API - Direct integration for custom tools and workflows
The MCP server fills a specific gap: conversational debugging with full production context. Sometimes you don't want errors overlaid in your editor, you want to discuss them with an AI that understands the bigger picture. That's where MCP shines.
And because MCP is an open standard, the Cazon server will work with ChatGPT, Copilot Chat, and other MCP-compatible assistants as they adopt the protocol. Build once, debug everywhere.
Getting Started
If you use Claude Desktop and want production-aware debugging:
- Get a Cazon API key at cazon.dev/dashboard
- Install the MCP server:
npm install -g @cazon/mcp-server - Add config to Claude Desktop (
~/Library/Application Support/Claude/claude_desktop_config.jsonon macOS) - Restart Claude and start asking about production errors
Full setup instructions are available in our MCP documentation. The MCP server is open source at github.com/cazon-ai/cazon if you want to see how it works or extend it.
What's Next
MCP brings production intelligence into Claude Desktop. But for developers who live in VS Code, we've built something more integrated: a native extension that shows errors inline as you code, with the same pattern matching and impact scoring, plus the ability to trigger AI analysis without leaving your editor.
Next, we'll show you how the VS Code extension works and why having errors visible in your editor changes how you debug.
This is Post 7 of our launch series on error intelligence. Follow along as we unpack the problem, introduce solutions, and show you how Cazon is building the missing intelligence layer between production errors and AI coding assistants.