mcp-criticagent

name

mcp-criticagent

description

Evaluate MCP (Model Context Protocol) servers and tools end to end — deploy from a GitHub URL or npm package, test protocol communication and tool calls, run AI-generated smart tests, and score repository sustainability and popularity. Use this skill whenever the user wants to test, evaluate, audit, score, or vet an MCP server, MCP tool, or MCP package, or asks "does this MCP work" or "is this MCP any good", even if they only paste a GitHub URL or a package name.

MCP-CriticAgent

You evaluate an MCP tool in three layers, each with its own verdict. Never merge them into one number without showing the parts.

Layer	Question it answers	How
1. Deploy + protocol	Does it start and speak MCP?	npm deployment, tools/list, first tool call
2. Behavior	Do its tools actually work?	AI smart tests (generated test cases, executed and verified)
3. Repository health	Will it still work next quarter?	GitHub sustainability + popularity scoring

Prerequisites

Run from the repository root (contains src/ and pyproject.toml).

• Python deps: uv sync (or pip install -e .)
• Node.js 18+ with npx (MCP tools are deployed as npm packages)
• Optional env in .env: DASHSCOPE_API_KEY for layer 2 smart tests (apply at https://help.aliyun.com/zh/model-studio/get-api-key). HUB_TOKEN (GitHub) is not required: layer 3 scoring works without it and only risks rate-limiting on heavy use. Supabase keys only if the user wants results exported.

Check what is available before promising layers: without a model key, layer 2 falls back to basic protocol tests; without HUB_TOKEN, layer 3 may be rate-limited.

Workflow

1. Identify the target. A GitHub URL goes to test-url; a bare npm package name (e.g. @upstash/context7-mcp) goes to test-package. If the user is exploring, list-tools --search <term> queries the built-in database of 5000+ known MCP tools.

2. Pick the flags for the situation (defaults enable everything):

# Full evaluation (all three layers, report saved):
uv run python -m src.main test-url "https://github.com/OWNER/REPO" --no-db-export

# Quick connectivity check only (fast, no model key needed):
uv run python -m src.main test-package "<package>" --no-smart --no-evaluate --no-db-export

# Add --verbose when the user wants to see what the tool calls returned.

Keep --no-db-export unless the user explicitly wants Supabase export. Deployment can take minutes for heavy packages; the default timeout is 600s.

3. Read the results. Reports land in data/test_results/ (JSON + HTML). Interpret for the user:

   • Layer 1: deployment success, protocol handshake, number of tools discovered, first-tool-call result. A tool that deploys but fails every call is broken regardless of its stars.
   • Layer 2: smart-test success rate (threshold 0.7). Quote failing test cases with their error messages as evidence.
   • Layer 3: final_score (0-100) = sustainability 50% (recency, commit frequency, stability, issue responsiveness/health) + popularity 50% (stars 70%, forks 30%). A high score with layer-1 failures means a popular repo whose current release is broken — say exactly that.

4. Report three layer verdicts with evidence, then a recommendation: use / use with caution / avoid. State which layers were skipped and why (missing key, timeout) rather than presenting a partial run as complete.

Caveats

• Deployment executes third-party npm packages: prefer running in a sandbox or container when the package is untrusted; never expose user secrets in the environment beyond the keys the run needs.
• The tool database (data/mcp.csv) is a snapshot; a missing entry means unknown, not bad. Use test-url for anything not in it.
• Layer 3 scores the repository, not the code quality of the MCP protocol implementation — layers 1-2 are the ground truth for "does it work".