The MCP (Model Context Protocol) ecosystem is growing fast. There are now hundreds of MCP servers available — but how do you know which ones are worth installing?
After building and evaluating 60+ MCP servers ourselves, we developed a practical checklist that saved us from shipping broken tools. Here's the framework we use.
Most MCP server listings tell you what the server does. Very few tell you how well it does it. You install something that sounds perfect, then discover:
Sound familiar?
Before installing any MCP server, ask these questions:
Question: Does this server activate when (and only when) it should?
Red flags:
Green flags:
Question: Does it pull the right context for the task?
Red flags:
Green flags:
Question: Are its conclusions tied to actual data?
Red flags:
Green flags:
Question: Does the output actually solve your problem?
Red flags:
Green flags:
Question: Does it learn from usage?
Red flags:
Green flags:
For each dimension, score 0-1:
| Dimension | Score | Notes |
|---|---|---|
| Trigger | _/1 | |
| Retrieval | _/1 | |
| Reasoning | _/1 | |
| Output | _/1 | |
| Feedback | _/1 | |
| Total | ||
| _/5 |
Our threshold: We don't ship anything scoring below 4/5 (0.8 per dimension).
From our evaluation of 60+ servers, here's the distribution of failures: Test with adversarial prompts first. Don't just test the happy path. Try prompts that are similar to the intended use case but shouldn't activate the server.
Check the README for failure documentation. Servers that document their limitations are usually higher quality than those that claim to handle everything.
Look for version history. Active maintenance is a strong signal. Abandoned servers with a single commit are rarely production-ready.
Try it in a sandbox first. Run it against 10 diverse prompts and check whether it handles edge cases gracefully.
We've open-sourced our evaluation framework and 50+ evaluated servers:
If you've developed your own evaluation criteria for MCP servers, I'd love to hear about it in the comments. This post is part of a series on building production-quality AI agent tools. The first post covered our Five Elements framework.