io.github.stufently/mws-s3-mcp-ai-cli

How clearly this server's own tool descriptions say what a tool does, and how consistent its tool list is. The first part is an AI model's yes/no reading of each description; the second part is counted. We do not turn it into a grade.

The AI items cover 14 of 14 tools: for a tool where two differently worded runs disagreed, that item is left out. They were not checked by hand, and no tool was called. “Easy to mix up” means two tools whose names or descriptions overlap without saying how they differ; the AI found none. We do not rate whether the tool set is complete or whether side effects are spelled out, because our AI reading of those was not reliable enough.

{
  "mcpServers": {
    "mws-s3-mcp-ai-cli": {
      "command": "docker",
      "args": [
        "run",
        "-i",
        "--rm",
        "ghcr.io/stufently/mws-s3-mcp-ai-cli:0.1.0"
      ]
    }
  }
}

{
  "mcpServers": {
    "mws-s3-mcp-ai-cli": {
      "command": "docker",
      "args": [
        "run",
        "-i",
        "--rm",
        "ghcr.io/stufently/mws-s3-mcp-ai-cli:0.1.0"
      ]
    }
  }
}

{
  "servers": {
    "mws-s3-mcp-ai-cli": {
      "type": "stdio",
      "command": "docker",
      "args": [
        "run",
        "-i",
        "--rm",
        "ghcr.io/stufently/mws-s3-mcp-ai-cli:0.1.0"
      ]
    }
  }
}

code --add-mcp '{"name":"mws-s3-mcp-ai-cli","type":"stdio","command":"docker","args":["run","-i","--rm","ghcr.io/stufently/mws-s3-mcp-ai-cli:0.1.0"]}'

claude mcp add --transport stdio mws-s3-mcp-ai-cli -- docker run -i --rm ghcr.io/stufently/mws-s3-mcp-ai-cli:0.1.0

npx @modelcontextprotocol/inspector@2.11.0 -- docker run -i --rm ghcr.io/stufently/mws-s3-mcp-ai-cli:0.1.0

Built from the server's registry entry with each client's documented format (checked 2026-10-14; the Cursor link and the VS Code command 2026-10-19; the MCP Inspector command 2026-10-21). Values in < > are yours to fill in; this page never contains a key. Where the server needs a setting the entry does not declare, it can still fail to start.

Facts about the publisher. We do not turn them into a trust score.

Source: the registry entry and api.github.com · 2026-10-23; security policy read from GitHub · 2026-10-23.

Publish this server? Run these checks against it yourself before release, from your own machine: a server the command starts, or your own server at its URL after a one-time proof that you control it: npx protocolprobe-selftest.

• com.oneuptime/mcpOneUptimealso has tools that reads or searchescompare with this one
• com.cloudflare.mcp/mcpalso has tools that reads or searchescompare with this one
• io.github.patrickchugh/terravisionTerraVisionalso has tools that reads or searchescompare with this one
• io.github.BetterDB-inc/monitoralso has tools that reads or searchescompare with this one
• io.github.TencentCloudBase/cloudbase-mcpCloudBasealso has tools that reads or searchescompare with this one
• io.github.Higangssh/homebutleralso has tools that reads or searchescompare with this one

Active servers whose topic is also “cloud-and-devops” and that have at least one tool of the same kind; most kinds in common first, then most GitHub stars. The topic and the tool kinds are an AI model's reading of the publishers' descriptions, not checked by hand. Similar is not a recommendation.

Not probed (read-only run)

We started the package in an isolated sandbox and read what it declares, but this test round was read-only, so its tools were not called. No security score is given; a check we did not run is never counted as passed.

Each check ends in exactly one state: exercised, not run (with the reason), or not applicable (with the reason). 3 of the 12 checks that apply to it were exercised. We do not publish security-check outcomes for named servers.

Source: our own test in an isolated sandbox, 2026-10-23, mcp-runtime-engine@0.1.0. Which checks count and why follows our published scoring model.

The steps in order, as the test record holds them.

1. 0 msStarted the published package in an isolated sandbox (read-only mode)
2. 164 msConnected after 0 redirects.
3. Read the inventory: 14 tools, 3 resources, 0 prompts.
4. 2,231 msFinished: 3 checks exercised.

One value per field, each with its source. Fields written by AI models are marked. A field with no value says why.

“Not stated” means the source did not state it; “does not apply” means it does not apply to this kind of server; “our limit” means our own lookup failed. A linked value opens the directory filtered to the same value.