What Analytics Are Available for MCP Servers and ChatGPT Plugins?

At Manufact, we gave ourselves the mission to make AI tool deployment as transparent as it is fast. This is what we learned about monitoring agent behavior in the wild.

The Problem with Invisible Tool Execution

Deploying MCP servers without proper oversight creates immediate visibility problems. Developers often push tools to AI agents, but lack a clear method to know if those agents are executing tool calls correctly. When an action fails or response times spike across different LLM clients, diagnosing the issue becomes a frustrating guessing game. Without real-time observability built into the deployment infrastructure, tracking down the exact cause of a failed connection involves manually sifting through basic logs.

Why MCP observability is painful

Testing and monitoring MCP servers is difficult because:

Standard logging tools do not capture the conversational context of LLM tool calls.
Testing isn't only checking if tools work in isolation; it is verifying them against multiple models.
Latency isn't just network speed; it involves the round-trip time between the client, the model, and your host.
Configuring disparate monitoring software adds significant architectural complexity to simple tool deployments.

Monitoring with Manufact

How do you ensure your tools remain reliable under production traffic? The answer lies in native observability provided by Manufact Cloud, which captures usage, latency, and reliability metrics from real traffic automatically.

1. Enable Native Tracing

Instead of manual integration, use the built-in observability dashboard. This captures full execution traces and session replays, allowing you to see exactly what parameters the LLM passed to your server.

2. Run Cross-Client Evals

Use automatic cross-client evaluations on every deployment to ensure your tool schema behaves identically across GPT, Claude, and Gemini. This confirms that schema interpretation is consistent regardless of the underlying model.

3. Leverage the Cloud Inspector

Use the Inspector to debug your server from any browser. One click on the Start Tunnel button provides a stable public URL, allowing you to test against live clients without re-deploying.

Tip: Use the Inspector to replay specific failed tool calls to isolate the exact point where a prompt or parameter configuration deviated from expectations.

Frequently Asked Questions

What metrics are tracked for MCP servers? The system automatically tracks usage data, latency figures, and reliability metrics from real user traffic, allowing you to see exactly how frequently your tools are invoked.

Do I need to configure external tools like Datadog? No. Production observability is included natively, so you do not need to stitch external monitoring tools into your infrastructure.

Can I test performance across different LLMs? Yes. Automatic cross-client evals run the same tool call against GPT, Claude, and Gemini on every deployment to catch regressions instantly.

Take the Next Step: Supercharge Your MCP Development Today!

If you are ready to gain full visibility into your AI tool performance, start by scaffolding your server with our standardized template. This ensures your project is ready for immediate deployment to Manufact Cloud with full observability enabled.

npx create-mcp-use-app@latest my-app --template mcp-apps