Mirobody
Lab results to LOINC codes and UCUM units, symptoms and diagnoses to ICPC-3. Offline, no API key.
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.
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。
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。
选择你使用的 AI 应用,然后复制设置。
你需要
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。
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。
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
注册名称已通过 GitHub 账户 jaimenbell 验证,该账户拥有此仓库
我们还没有从 GitHub 读取这个仓库
我们还没有从 GitHub 读取这个仓库
它在你的电脑上运行,没有互联网端点
在你的电脑上运行,改用本地设置
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。
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。
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)。
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。
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)。
同一分类中有:Mirobody, FHIR MCP Server, Google Health MCP
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
Lab results to LOINC codes and UCUM units, symptoms and diagnoses to ICPC-3. Offline, no API key.
MCP server providing seamless access to FHIR APIs for AI tools and healthcare applications
Privacy-first unofficial Google Health API v4 MCP server for health, sleep, activity, HR agents
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。
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.
// ~/.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"] } } }
| Tool | What it does | What 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_status | nvidia-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_status | systemctl 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_config | Read-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). |
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.
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.
Read this before you register vllm-ops-mcp. Honesty about blast radius is the point.
shlex.quote wraps the one caller-adjacent path that does reach a shell string, the unit name in systemctl show).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."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).restart_service doesn't exist in this codebase yet (see Future extension above), so there's nothing to accidentally enable.| Claim | Implementation | Verified by |
|---|---|---|
Liveness health tier (/v1/models) | vllm_ops_mcp/probes.py::_probe_models, check_health | tests/test_probes.py::TestCheckHealthLiveness; live: tests/test_live_smoke.py::test_live_check_health_liveness |
| Deep/real-completion health tier | vllm_ops_mcp/probes.py::_probe_completion, check_health(deep=True) | tests/test_probes.py::TestCheckHealthDeep; live: test_live_check_health_deep |
| Model list passthrough | probes.py::list_models | tests/test_probes.py::TestListModels; live: test_live_list_models |
| On-demand completion sanity check | probes.py::test_completion | tests/test_probes.py::TestTestCompletion; live: test_live_test_completion |
| GPU/VRAM status via nvidia-smi | probes.py::get_gpu_status | tests/test_probes.py::TestGetGpuStatus; live: test_live_get_gpu_status |
| Per-process VRAM attribution | probes.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 show | probes.py::get_service_status | tests/test_probes.py::TestGetServiceStatus; live: test_live_get_service_status |
Live-process launch-flag introspection via /proc/ /cmdline | probes.py::get_serve_config, parse_launch_flags | tests/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 down | probes.py::get_serve_config, _parse_exec_script_text | tests/test_probes.py::TestGetServeConfig::test_falls_back_to_exec_script_when_service_down |
| Local config-mirror override path | probes.py::get_serve_config + config.serve_config_override_path | tests/test_probes.py::TestGetServeConfig::test_uses_local_override_file, test_override_file_missing_reports_error |
Sensitive-flag-value redaction in get_serve_config | probes.py::redact_argv, redact_raw_text, config.SENSITIVE_ARGV_FLAG_PATTERNS | tests/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_limit | tests/test_config.py::TestTokenBucket, TestDeepRateLimiter |
| WSL-vs-native shell-out abstraction | probes.py::_shell_args, config.runs_in_wsl | tests/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 present | vllm_ops_mcp/server.py | tests/test_server.py |
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.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.\ 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