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.
The Problem
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:
- It activates on the wrong prompts (false positives)
- It pulls irrelevant context (retrieval drift)
- It sounds confident but gives wrong answers (ungrounded reasoning)
- It never improves from feedback
Sound familiar?
The 5-Dimension Evaluation Checklist
Before installing any MCP server, ask these questions:
1. Trigger Precision
Question: Does this server activate when (and only when) it should?
Red flags:
- Overly broad trigger descriptions ("use for anything related to X")
- No documented activation conditions
- Activates on common words that appear in unrelated contexts
Green flags:
- Specific, documented trigger scenarios
- Clear non-activation cases listed
- Tested against diverse prompts
2. Retrieval Quality
Question: Does it pull the right context for the task?
Red flags:
- Returns large chunks without filtering
- No citation or source tracking
- Retrieves plausible but outdated information
Green flags:
- Targeted, minimal context retrieval
- Source attribution for every piece of context
- Version-aware (knows when data might be stale)
3. Reasoning Grounding
Question: Are its conclusions tied to actual data?
Red flags:
- Generates advice without referencing specific inputs
- Can't explain its reasoning chain
- Confident answers that contradict its own retrieved context
Green flags:
- Every conclusion references specific evidence
- Explicitly flags uncertainty
- Gracefully handles missing information
4. Output Usefulness
Question: Does the output actually solve your problem?
Red flags:
- Generic responses that could apply to any input
- Repeats the question back without adding value
- Requires significant post-processing to be actionable
Green flags:
- Directly actionable output
- Adapted to specific input context
- Includes edge cases and limitations
5. Feedback Integration
Question: Does it learn from usage?
Red flags:
- Same mistake repeated across sessions
- No mechanism for user corrections
- Ignores conversation history
Green flags:
- Adapts behavior based on user corrections
- Tracks what worked and what didn't
- Progressive improvement over time
Quick Scoring Template
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).
Real Example: What Failure Looks Like
From our evaluation of 60+ servers, here's the distribution of failures:
- Retrieval drift (40%): The #1 failure mode. Server pulls plausible but incorrect context.
- Trigger false positives (25%): Activates too often, eroding user trust.
- Reasoning without grounding (20%): Sounds logical but isn't tied to data.
- Feedback absence (15%): Same mistakes repeated indefinitely.
Practical Tips
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.
Resources
We've open-sourced our evaluation framework and 50+ evaluated servers:
- GitHub: github.com/tancoai/lianzhu-skill
- Platform with evaluated servers: tancoai.com
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.
Top comments (1)
I appreciate the emphasis on evaluating MCP servers based on their ability to provide high-quality context retrieval, as highlighted in the Retrieval Quality dimension. The green flags mentioned, such as targeted context retrieval and source attribution, are crucial in ensuring that the server provides accurate and reliable information. In my experience, retrieval drift can be a significant issue, and I've found that servers that prioritize version-awareness and cite their sources tend to perform better in real-world scenarios. Have you considered adding any additional dimensions to the evaluation checklist, such as security or scalability, to further assess the suitability of an MCP server for production environments?