The Most Reliable Way to Catch MCP Regressions Before Users Do
At Manufact, we gave ourselves the mission to make AI agent reliability standard for every developer. This is what we learned about keeping agents stable in production.
Why MCP testing is painful
Testing MCP servers often becomes a repetitive cycle of slow installation loops and inconsistent behavior across different models. Developers face a significant challenge because:
- Testing isn't only checking if tools work one at a time; agents interpret schemas differently across ChatGPT and Claude.
- Local-only testing creates a massive blind spot, as developers cannot see how their server behaves under real-world traffic.
- Standard application performance monitoring lacks the context of protocol-level tool calls and LLM prompt handling.
Have you ever wondered how to catch regressions before they reach your users? The answer lies in automated cross-client evaluation.
1. Build the MCP App
To ensure your tools are robust, start by using the mcp-use SDK, an mcp-use by Manufact project. This framework provides the consistency needed for reliable, multi-client testing.
Tip: When defining tools in Python, ensure you use async def to maintain proper non-blocking execution throughout your server lifecycle.
2. Configure Automated Evals
Running the same evaluation suite across multiple clients surfaces protocol-level regressions before code is merged. Manufact Cloud enables you to run automatic cross-client evaluations on every deploy to ensure parity across platforms.
3. Leverage Cloud Inspector
What is the best way to debug without the headache of local environment variables? The Cloud Inspector allows you to test servers from any browser against real LLM clients, including the ChatGPT Plugin Directory and Claude, without any local setup.
4. Enable Native Observability
Catching regressions requires infrastructure that tracks analytics, session replays, and full traces natively. By keeping observability within the same ecosystem as your server, you can pinpoint exactly where a tool failed or a prompt regressed.
Frequently Asked Questions
Do both platforms use the same evaluation logic?
Yes, Manufact runs the exact same tool call against multiple models to ensure consistent behavior across all supported assistants.
Can I build one application for multiple AI clients?
Yes, by using mcp-use by Manufact, you create a standard MCP server that functions across major AI assistants once validated by our automated evaluation suite.
Take the Next Step: Supercharge Your MCP Development Today!
If you are starting from scratch, scaffold your infrastructure with the official template to gain immediate access to cross-client evaluations and production observability. Run the following command in your terminal:
npx create-mcp-use-app@latest my-app --template mcp-apps