Mirobody
Lab results to LOINC codes and UCUM units, symptoms and diagnoses to ICPC-3. Offline, no API key.
제작자가 쓴 설명(영어 원문)
Read-only ops/health MCP server for a local vLLM instance: health tiers, GPU/VRAM, service status.
이 세 가지는 절대 하나의 점수로 합치지 않아요. 의견, 측정, 인기는 서로 다른 것이니까요.
이 서버는 아직 쉬운 말로 된 요약을 쓰지 못했어요. 아래는 레지스트리에서 읽어 온 사실이에요.
쓰고 있는 AI 앱을 고른 다음 설정을 복사하세요.
필요한 것
어떤 앱을 쓰세요?
위에서 앱을 고르세요. 기억해 두었다가 다음부터는 버튼에 “…에 설치”라고 표시해요.
Claude Desktop → Settings → Developer → Edit Config를 열고 claude_desktop_config.json에 이 내용을 붙여 넣으세요
{
"mcpServers": {
"vllm-ops": {
"command": "uvx",
"args": [
"vllm-ops-mcp"
]
}
}
}터미널에서 이 명령어를 실행하세요
claude mcp add vllm-ops -- uvx vllm-ops-mcp프로젝트의 .cursor/mcp.json 또는 ~/.cursor/mcp.json에 붙여 넣으세요
{
"mcpServers": {
"vllm-ops": {
"command": "uvx",
"args": [
"vllm-ops-mcp"
]
}
}
}브라우저가 앱을 열지 물어본 다음, 앱이 설정을 보여 주고 확인을 요청해요. 키 값은 비워 두니 직접 입력하세요.
프로젝트의 .vscode/mcp.json에 붙여 넣으세요
{
"servers": {
"vllm-ops": {
"type": "stdio",
"command": "uvx",
"args": [
"vllm-ops-mcp"
]
}
}
}브라우저가 앱을 열지 물어본 다음, 앱이 설정을 보여 주고 확인을 요청해요. 키 값은 비워 두니 직접 입력하세요.
이 서버는 내 컴퓨터에서 실행되는데, ChatGPT는 인터넷에 있는 서버에만 연결할 수 있어요. 다른 앱을 고르세요.
선택한 앱은 이 기기의 쿠키에만 저장돼요. 계정은 필요 없어요.
이 설정은 제작자의 레지스트리 정보로 만들었어요. README와 다르면 README를 따르세요.
내 컴퓨터에서 실행돼요. 설치하면 AI 앱에 도구 목록이 나타나요.
누구나 볼 수 있는 정보를 점검했고, 코드는 읽지 않았어요. 해당하지 않는 항목은 개수에서 뺐어요. · 2026년 11월 8일 점검
레지스트리 이름이 GitHub 계정 jaimenbell 기준으로 확인되었고, 이 계정이 저장소를 소유하고 있어요
아직 GitHub에서 이 저장소를 읽어 오지 않았어요
아직 GitHub에서 이 저장소를 읽어 오지 않았어요
내 컴퓨터에서 실행되는 서버라 인터넷 엔드포인트가 없어요
내 컴퓨터에서 실행되며, 대신 로컬 설정을 사용해요
내 컴퓨터에서 실행돼요. 도구 목록은 인터넷 엔드포인트가 있는 서버에서만 읽을 수 있어요
레지스트리에서 이 카테고리의 활성 서버 전체와 비교했어요. 스타는 게시자가 저장소를 소유한 경우에만 세요.
해당하는 기본 점검 1개 중 1개를 통과했어요. 코드는 읽지 않았어요. 키나 토큰을 넣기 전에 서버가 어떤 권한을 요구하는지 확인하고, 꼭 필요한 권한만 있는 키를 쓰세요.
레지스트리에 필수 키가 나와 있지 않아요. 다만 처음 쓸 때 로그인을 요구하는 서버도 있어요.
내 컴퓨터에서 실행돼요. AI 앱이 필요할 때 실행하고, 데이터는 제작자의 서버를 거치지 않아요(서버 자체가 온라인 서비스를 호출하는 경우는 제외).
GitHub 저장소는 마지막으로 – 변경됐고, 최신 버전(0.1.0)은 4개월 전 레지스트리에 올라왔어요.
Claude Desktop, Claude Code, Cursor, VS Code처럼 MCP를 지원하는 앱에서 쓸 수 있어요. 내 컴퓨터에 Python (uv)도 있어야 해요.
같은 카테고리에는: Mirobody, FHIR MCP Server, Google Health MCP
같은 카테고리에서 다른 제작자가 만든 서버예요. 스타 순으로 정렬했어요.
카드 보는 법:★ GitHub 스타i세 기둥: 리뷰 · 점검 · 사용 정도i업데이트 = 최신 버전을 레지스트리에 공개한 날i
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
제작자의 저장소에서 쓰인 그대로 가져왔어요. PopNx MCP가 검토하거나 보증한 내용이 아니에요. HTML, 이미지, 스크립트는 제거했어요. 링크를 누르면 이 사이트를 벗어나요.
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 sufficiently앞부분만 보여 드려요. 나머지는 저장소에서 읽어 보세요