Mirobody
Lab results to LOINC codes and UCUM units, symptoms and diagnoses to ICPC-3. Offline, no API key.
Descripción del creador (original en inglés)
Read-only ops/health MCP server for a local vLLM instance: health tiers, GPU/VRAM, service status.
Estos tres nunca se combinan en una sola puntuación: las opiniones, las mediciones y la popularidad son cosas distintas.
Todavía no escribimos un resumen sencillo para este. Abajo están los datos que leímos del registro.
Elige la app de IA que usas y copia la configuración.
Necesitas
¿Qué app usas?
Elige tu app arriba. La recordamos, así que la próxima vez el botón dirá “Instalar en …”.
Abre Claude Desktop → Settings → Developer → Edit Config y pega esto en claude_desktop_config.json
{
"mcpServers": {
"vllm-ops": {
"command": "uvx",
"args": [
"vllm-ops-mcp"
]
}
}
}Ejecuta esto en tu terminal
claude mcp add vllm-ops -- uvx vllm-ops-mcpPégalo en .cursor/mcp.json de tu proyecto, o en ~/.cursor/mcp.json
{
"mcpServers": {
"vllm-ops": {
"command": "uvx",
"args": [
"vllm-ops-mcp"
]
}
}
}Tu navegador te pide permiso para abrir la app; luego la app muestra la configuración y te pide confirmar. Los valores de las claves quedan vacíos para que los completes tú.
Pégalo en .vscode/mcp.json de tu proyecto
{
"servers": {
"vllm-ops": {
"type": "stdio",
"command": "uvx",
"args": [
"vllm-ops-mcp"
]
}
}
}Tu navegador te pide permiso para abrir la app; luego la app muestra la configuración y te pide confirmar. Los valores de las claves quedan vacíos para que los completes tú.
Este se ejecuta en tu computadora, y ChatGPT solo puede conectarse a servidores en internet. Elige otra app.
Tu elección se guarda en una cookie en este dispositivo; no necesitas cuenta.
Esta configuración se genera a partir de la ficha del creador en el registro. Si el README dice otra cosa, sigue el README.
Se ejecuta en tu computadora; la lista de herramientas aparece en tu app de IA después de instalarlo.
Revisamos información que cualquiera puede ver; no hemos leído el código. Las revisiones que no aplican no se cuentan. · Revisado el 8 nov 2026
El nombre en el registro se verificó con la cuenta de GitHub jaimenbell, dueña del repo
Todavía no hemos leído este repo en GitHub
Todavía no hemos leído este repo en GitHub
Este se ejecuta en tu computadora; no hay endpoint en internet
Se ejecuta en tu computadora y usa ajustes locales en su lugar
Se ejecuta en tu computadora; solo podemos leer listas de herramientas de endpoints en internet
Comparado con todos los servidores activos de esta categoría en el registro. Las estrellas solo cuentan cuando quien lo publica es dueño del repo.
Superó 1 de 1 revisiones básicas aplicables. No hemos leído el código. Antes de agregar una clave o un token, revisa qué acceso pide el servidor y usa una clave que tenga solo los permisos que necesitas.
El registro no indica ninguna clave obligatoria, aunque algunos servidores te piden iniciar sesión la primera vez que los usas.
Se ejecuta en tu computadora. Tu app de IA lo inicia cuando hace falta, y los datos no pasan por los servidores del creador (a menos que el propio servidor llame a un servicio en línea).
El repo de GitHub cambió por última vez –, y la última versión (0.1.0) llegó al registro hace 4 meses.
Las apps compatibles con MCP, como Claude Desktop, Claude Code, Cursor y VS Code. También necesitas Python (uv) en tu computadora.
En la misma categoría: Mirobody, FHIR MCP Server, Google Health MCP
Servidores de la misma categoría hechos por otros creadores, ordenados por estrellas.
Cómo leer una tarjeta:★ Estrellas de GitHubiTres pilares: reseñas · revisiones · usoiActualizado = fecha en que se publicó la última versión en el registroi
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
Copiado tal cual del repo del creador. PopNx MCP no lo ha revisado ni lo respalda. Se quitaron HTML, imágenes y scripts. Los enlaces llevan fuera de este sitio.
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 sufficientlySolo se muestra el principio; lee el resto en el repo