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Vllm Ops MCP

作者:jaimenbell版本 0.1.0i★ –i在你的电脑上运行
注册名称io.github.jaimenbell/vllm-ops-mcp

安装

概览

作者提供的说明(英文原文)

Read-only ops/health MCP server for a local vLLM instance: health tiers, GPU/VRAM, service status.

PopNx MCP 的三大支柱

这三项永远不会合并成一个分数:意见、测量和人气是不同的东西。

它能帮你做什么

我们还没有为它写通俗的介绍。下面是我们从注册中心读取到的事实。

  • 连接方式: 在你的电脑上运行
  • 传输方式: stdio
  • 分类: 诊所与健康

安装

选择你使用的 AI 应用,然后复制设置。

安装之前

  • 基础检查:1 项中通过 1 项
  • 在你的电脑上运行,拥有你的用户账户的权限(文件、网络、应用)。
  • 注册中心没有列出需要填写的密钥。

你需要

  • PyPI已安装 Python 的 uv(用于 uvx 命令)

你用的是哪个应用?

在上方选择你的应用。我们会记住它,下次按钮会显示“安装到 …”。

打开 Claude Desktop → Settings → Developer → Edit Config,把下面的内容粘贴到 claude_desktop_config.json

{
  "mcpServers": {
    "vllm-ops": {
      "command": "uvx",
      "args": [
        "vllm-ops-mcp"
      ]
    }
  }
}

在终端中运行这条命令

claude mcp add vllm-ops -- uvx vllm-ops-mcp

在 Cursor 中打开

或者自己复制设置

粘贴到项目中的 .cursor/mcp.json,或 ~/.cursor/mcp.json

{
  "mcpServers": {
    "vllm-ops": {
      "command": "uvx",
      "args": [
        "vllm-ops-mcp"
      ]
    }
  }
}

浏览器会先询问是否打开应用,然后应用会显示设置并请你确认。密钥的值会留空,由你自己填写。

在 VS Code 中打开

或者自己复制设置

粘贴到项目中的 .vscode/mcp.json

{
  "servers": {
    "vllm-ops": {
      "type": "stdio",
      "command": "uvx",
      "args": [
        "vllm-ops-mcp"
      ]
    }
  }
}

浏览器会先询问是否打开应用,然后应用会显示设置并请你确认。密钥的值会留空,由你自己填写。

这个服务器在你的电脑上运行,而 ChatGPT 只能连接互联网上的服务器。请选择其他应用。

你的选择通过 Cookie 保存在这台设备上,无需账户。

这些设置是根据作者在注册中心的条目生成的。如果 README 的写法不同,请以 README 为准。

它能做什么

它在你的电脑上运行;安装后,工具列表会显示在你的 AI 应用中。

基础检查i

我们检查的是任何人都能看到的信息;我们没有阅读代码。不适用的检查项不计入总数。 · 检查于 2026年11月8日

  • ✓ 通过

    作者与仓库一致

    注册名称已通过 GitHub 账户 jaimenbell 验证,该账户拥有此仓库

  • … 尚未检查

    仍在维护

    我们还没有从 GitHub 读取这个仓库

  • … 尚未检查

    有许可证

    我们还没有从 GitHub 读取这个仓库

  • – 不适用

    端点有响应

    它在你的电脑上运行,没有互联网端点

  • – 不适用

    使用 OAuth 登录

    在你的电脑上运行,改用本地设置

  • … 尚未检查

    工具描述中没有隐藏指令

    它在你的电脑上运行;我们只能读取互联网端点的工具列表

与“诊所与健康”中的其他服务器比较i

  • 星标数多于此分类中其他 1028 个服务器的 0%
  • 发布到注册中心的时间比此分类中其他 1028 个服务器的 23% 更新
  • 基础检查结果等于或优于此分类中其他 1028 个服务器的 100%

与注册中心里这个分类中所有活跃的服务器比较。只有当发布者拥有该仓库时,星标数才计入。

常见问题

Vllm Ops MCP 安全吗?

它在 1 项适用的基础检查中通过了 1 项。我们没有阅读代码。在添加密钥或令牌之前,请先看看这个服务器要求哪些权限,并使用只具备所需权限的密钥。

它需要哪些密钥或代码?

注册中心没有列出必需的密钥,不过有些服务器会在首次使用时要求你登录。

它在我的电脑上运行还是在云端?

它在你的电脑上运行。你的 AI 应用会在需要时启动它,数据不会经过作者的服务器(除非它自己调用在线服务)。

它还在维护吗?

GitHub 仓库最后更新于–,最新版本(0.1.0)于4个月前进入注册中心。

哪些应用可以使用它?

支持 MCP 的应用,例如 Claude Desktop、Claude Code、Cursor 和 VS Code。你的电脑上还需要有 Python (uv)。

有什么可以替代它?

同一分类中有:Mirobody, FHIR MCP Server, Google Health MCP

替代选择

同一分类中其他作者的服务器,按星标数排序。

如何看卡片:↓ 每周下载量i★ GitHub 星标i三大支柱:评价 · 检查 · 使用情况i更新 = 最新版本发布到注册中心的时间i

Mirobody

Lab results to LOINC codes and UCUM units, symptoms and diagnoses to ICPC-3. Offline, no API key.

作者:thetahealth正在增长↓ 290/周★ 1408昨天更新

在你的电脑上运行✓ thetahealth.ai

FHIR MCP Server

MCP server providing seamless access to FHIR APIs for AI tools and healthcare applications

作者:wso2正在增长↓ 823/周★ 13710天前更新

在你的电脑上运行

Google Health MCP

Privacy-first unofficial Google Health API v4 MCP server for health, sleep, activity, HR agents

作者:davidmosiah正在增长↓ 1024/周★ 794个月前更新

在你的电脑上运行

作者的 README

打开 README

按原文从作者的仓库复制。PopNx MCP 没有审查或背书其内容。HTML、图片和脚本已被移除。链接会离开本站。

vllm-ops-mcp

Read-only ops/health MCP server for a local, WSL2/systemd-managed vLLM server -- liveness vs. real-completion health tiers, GPU/VRAM status, systemd service status, and live serve-flag introspection. Built to the same standard as mcp-factory and desktop-mcp, and other local tooling in this operator's portfolio: own pyproject, own fastmcp server, honest README, real test suite, no product code before a spec was confirmed.

This is not a container-lifecycle manager. It assumes vLLM already runs as a bare-metal systemd unit inside WSL2 (this operator's actual setup -- see vllm-autostart), not inside Docker/Podman.

Quickstart

// ~/.claude.json (or any MCP-host stdio client config)

{ "mcpServers": { "vllm-ops-mcp": { "type": "stdio", "command": "C:\\Users\\ \\projects\\vllm-ops-mcp\\.venv\\Scripts\\python.exe", "args": ["C:\\Users\\ \\projects\\vllm-ops-mcp\\run_server.py"] } } }

Tools (Phase 1 -- all read-only, no gating required beyond a throughput cap)

ToolWhat it doesWhat it can't do
check_health(deep: bool = False)deep=False: GET /v1/models liveness check (same signal as llm_router.LocalLLM.is_healthy()). deep=True: additionally fires one minimal real /v1/chat/completions call (mirrors check-vllm.cmd's two-stage probe) to confirm the server actually *generates*, not just *reports a model loaded*.Cannot fix a degraded server -- reports ground truth only. deep=True is rate-limited (default 20/min, see below).
list_models/v1/models passthrough.No detail beyond what vLLM's API itself exposes.
test_completion(prompt, max_tokens=16)On-demand real completion with a caller-supplied prompt, for manual sanity checks.Rate-limited (same bucket as check_health(deep=True)); refuses if the liveness check fails first (no point firing a completion at a server that isn't even listening). max_tokens is clamped server-side to 1024 (config.MAX_TEST_COMPLETION_TOKENS) and the prompt is capped at 8000 chars (config.MAX_TEST_COMPLETION_PROMPT_CHARS, oversized prompts are rejected, not truncated) -- the rate limiter only bounds call *frequency*, not the cost of a single call.
get_gpu_statusnvidia-smi wrapper: per-GPU VRAM used/total + utilization%, plus per-process VRAM via --query-compute-apps.Per-process VRAM attribution does not work on this operator's actual WSL2 setup -- live-verified empty even while the vLLM process holds 13.7GB (see Limitations). Treat processes as best-effort/often-empty, not a reliable per-PID breakdown.
get_service_statussystemctl show -p ... (structured property output, not free-text parsing) via wsl -d from the Windows host, or native systemctl if this server itself runs inside WSL2/Linux. Returns load/active/sub state, restart count, main PID, last-active timestamp.Read-only -- cannot start/stop/restart the unit. No computed uptime duration (deliberately -- see Limitations, clock-discipline note).
get_serve_configRead-only launch-flag introspection. Prefers the LIVE process's actual argv (/proc/ /cmdline of the systemd unit's MainPID -- ground truth of what's running right now), falling back to the static exec script (local mirror via VLLM_OPS_MCP_SERVE_CONFIG_PATH, else a live cat of the WSL-side exec script) only when the process isn't up.vLLM doesn't expose its own launch flags over the API, so this never queries the server itself -- it's always shelling out. Exec-script fallback parsing is best-effort shell-text parsing (less precise than the live-argv path).
Future extension: restart_service (Phase 2, not built)

Deliberately not implemented in this release. Per the spec: vLLM is shared infra other bots/tools depend on (options-bot, qwen_cli.py callers, DeerFlow fallback), so a restart tool needs its own gating design (off by default, VLLM_OPS_MCP_ENABLE_RESTART=1, refuse to touch a healthy instance unless force=True, rate-capped) before it ships -- not retrofitted onto a read-only MVP. Tracked as a v2 idea, not a current capability. If built, it should wrap run-vllm.cmd's exact recipe (systemctl start vllm only, never blind stop/restart) rather than reinvent it.

The differentiator: probe HTTP, not the listener

Every health signal in this server comes from an actual HTTP request to the OpenAI-compatible API (urllib.request, not a socket/connect check). This is deliberate, not incidental: Get-NetTCPConnection and other listener-based checks miss WSL2-mirrored-networking-relayed ports entirely -- a genuinely healthy 127.0.0.1:8000 can be invisible to a listener probe run from the Windows host. check-vllm.cmd already embodies this lesson; this server generalizes it into two tiers (liveness vs. real generation) that nothing else in this operator's fleet exposes as MCP tools.

Security

Read this before you register vllm-ops-mcp. Honesty about blast radius is the point.

  • This server shells out to `nvidia-smi`, `systemctl` (via `wsl -d`), and reads `/proc/ /cmdline`. All six tools are read-only -- nothing here starts, stops, restarts, or kills a process, and no tool accepts an argument that gets passed to a shell unescaped (unit/distro names come from server-side config, not caller input; shlex.quote wraps the one caller-adjacent path that does reach a shell string, the unit name in systemctl show).
  • `check_health(deep=True)` and `test_completion` fire a real inference call against a shared GPU server. Rate-limited (default 20 calls/min, VLLM_OPS_MCP_DEEP_RATE_LIMIT_PER_MIN) specifically because this vLLM instance is shared infra other bots/tools depend on -- a caller hammering this MCP could starve or slow other consumers even though every call individually looks "read-only."
  • `get_serve_config`'s live-process path reads `/proc/ /cmdline`, which can include secrets if any were ever passed as CLI flags (none are, in this operator's actual setup -- the model path and flags are all non-secret; auth, if any, is environment-injected inside the exec script, not passed as an argv flag). Defense-in-depth: any argv entry whose preceding flag name matches a known-sensitive pattern (token, key, secret, password, passwd, credential -- config. SENSITIVE_ARGV_FLAG_PATTERNS) has its value redacted to [REDACTED] before being returned, in both the structured argv/flags fields and raw_text; the flag name itself is left visible so the shape is still diagnosable. This applies across all three sources (live_process, exec_script, local_config_mirror).
  • No tool has any write/mutate capability in this release. There is no env var that turns one on -- restart_service doesn't exist in this codebase yet (see Future extension above), so there's nothing to accidentally enable.

Honest-capabilities table

ClaimImplementationVerified by
Liveness health tier (/v1/models)vllm_ops_mcp/probes.py::_probe_models, check_healthtests/test_probes.py::TestCheckHealthLiveness; live: tests/test_live_smoke.py::test_live_check_health_liveness
Deep/real-completion health tiervllm_ops_mcp/probes.py::_probe_completion, check_health(deep=True)tests/test_probes.py::TestCheckHealthDeep; live: test_live_check_health_deep
Model list passthroughprobes.py::list_modelstests/test_probes.py::TestListModels; live: test_live_list_models
On-demand completion sanity checkprobes.py::test_completiontests/test_probes.py::TestTestCompletion; live: test_live_test_completion
GPU/VRAM status via nvidia-smiprobes.py::get_gpu_statustests/test_probes.py::TestGetGpuStatus; live: test_live_get_gpu_status
Per-process VRAM attributionprobes.py::get_gpu_status (--query-compute-apps)Unit-tested with a mocked non-empty response; live-verified to return EMPTY on this operator's actual WSL2 setup -- see Limitations
systemd unit status via structured systemctl showprobes.py::get_service_statustests/test_probes.py::TestGetServiceStatus; live: test_live_get_service_status
Live-process launch-flag introspection via /proc/ /cmdlineprobes.py::get_serve_config, parse_launch_flagstests/test_probes.py::TestGetServeConfig, TestParseLaunchFlags; live: test_live_get_serve_config -- live-confirmed it recovers flags (--enable-auto-tool-choice --tool-call-parser qwen3_xml) that were not in this repo's own grounding spec, proving the live-argv approach beats trusting a static doc
Exec-script fallback when the unit is downprobes.py::get_serve_config, _parse_exec_script_texttests/test_probes.py::TestGetServeConfig::test_falls_back_to_exec_script_when_service_down
Local config-mirror override pathprobes.py::get_serve_config + config.serve_config_override_pathtests/test_probes.py::TestGetServeConfig::test_uses_local_override_file, test_override_file_missing_reports_error
Sensitive-flag-value redaction in get_serve_configprobes.py::redact_argv, redact_raw_text, config.SENSITIVE_ARGV_FLAG_PATTERNStests/test_probes.py::TestGetServeConfig::test_live_process_redacts_sensitive_flag_values, test_exec_script_raw_text_redacts_sensitive_flag_values, test_local_override_file_redacts_sensitive_flag_values
Deep-inference rate limiting (shared-infra protection)config.py::TokenBucket, DeepRateLimiter, check_deep_rate_limittests/test_config.py::TestTokenBucket, TestDeepRateLimiter
WSL-vs-native shell-out abstractionprobes.py::_shell_args, config.runs_in_wsltests/test_config.py::TestRunsInWsl; tests/test_probes.py::TestGetServiceStatus::test_uses_wsl_prefix_on_windows, test_no_wsl_prefix_when_native
All 6 tools registered, no Phase-2 tool presentvllm_ops_mcp/server.pytests/test_server.py

Limitations (read before relying on this)

  • Per-process VRAM attribution is unreliable under this operator's actual WSL2 GPU-passthrough config. get_gpu_status's live smoke test (real nvidia-smi.exe against the real running vLLM process) returned an empty processes list even though the same call's gpus[0]. memory_used_mib correctly showed 13.7GB in use by that process. This is a documented nvidia-smi --query-compute-apps limitation under some WSL2/passthrough configurations, not a bug in this server's parsing -- the tool reports the empty result honestly (ok: true, processes: []) rather than fabricating attribution. Don't rely on processes for VRAM-overshoot root-causing on this setup; gpus[].memory_used_mib is the reliable signal.
  • No computed service uptime. get_service_status returns the raw ActiveEnterTimestamp string from systemctl (already timezone-labeled, e.g. MDT) rather than computing an elapsed duration server-side -- deliberately, per this operator's own clock-discipline rule (never state an elapsed/ETA time from estimation; artifact timestamps are often UTC while local is UTC-6/-7). Compute the delta at the call site if you need it, with an explicit timezone conversion.
  • `get_serve_config`'s exec-script fallback is best-effort shell-text parsing, not an exact argv array -- it handles \ line continuations and basic quoting via shlex.split, but a sufficiently

只显示了开头部分;其余内容请在仓库中阅读

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