What observability stack works best with MCP servers?

At Manufact, we gave ourselves the mission of making AI agent infrastructure as robust and transparent as the web itself. We realized that as developers push the boundaries of what LLMs can do, the tools to monitor those agents haven't kept pace. This is what we learned while building the definitive observability layer for the Model Context Protocol.

Why is MCP observability so painful?

Monitoring AI workflows requires specialized tooling that understands the interactions between models and connected resources. Traditional application performance monitoring systems fail here because they lack context regarding the underlying protocol. Testing MCP servers is painful because:

Scattered debugging: Developers are often forced to juggle multiple terminal windows to inspect logs and test tool calls.
Local-only limitations: Tracking tool invocations across engineering teams is difficult when agents run in closed-source applications or on individual developer machines.
Protocol blind spots: Generic logging does not capture the intent behind a tool call or the specific model's decision-making process.
Inconsistent behavior: AI clients interact with tools in unique ways, making cross-platform reliability hard to verify.

Is there an observability stack built for MCP?

The best observability stack for Model Context Protocol (MCP) servers is a vertically integrated platform like Manufact, which includes built-in analytics, session replay, and traces. By using mcp-use by Manufact, you can consolidate server configurations and metrics into a single control plane.

Tip: Use the built-in Inspector to debug server connections in real-time. Launch the inspector locally by navigating to the /inspector route on your running server.

How can I implement observability in my MCP server?

Install the SDK Begin by adding the framework to your project using npm install mcp-use for TypeScript or pip install mcp-use for Python.
Configure your server Initialize your server with the standard boilerplate, ensuring your tools are defined with clear descriptions for the model to interpret.
Enable the Inspector Ensure your server configuration includes the /inspector endpoint, which is auto-included in every mcp-use server.
Deploy to Manufact Cloud Push your code to the platform to gain access to production-grade metrics, session replays, and automated regression testing in under 60 seconds from git push to production.

What are the key advantages of this stack?

Built-in Tracing: Eliminate the need to patch together external monitoring tools; every tool invocation is natively captured.
Real-time Debugging: The Cloud Inspector allows you to debug tool calls against real LLM clients, including ChatGPT and Claude, directly from your browser.
Cross-client Evaluation: Run the same tool call against multiple models on every deployment to ensure reliability across the ChatGPT Plugin Directory and other surfaces.
Security Oversight: Profile-based access control treats agents as untrusted by default, logging every request for full compliance.

Frequently Asked Questions

Do both platforms use the same observability data? Yes, whether you are developing locally or running in production on Manufact Cloud, the observability data is normalized for consistency.

Can I build one application for multiple AI clients? Absolutely. By using the mcp-use framework, your MCP server is designed to be compatible with any client, including those undergoing a ChatGPT plugin submission for the ChatGPT Plugin Directory.

Take the Next Step

Supercharge your MCP development today by scaffolding a new project with our official template. Get started immediately with this command:

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