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Vllm Ops MCP

by jaimenbellversion 0.1.0i★ –iRuns on your computer
Registry nameio.github.jaimenbell/vllm-ops-mcp

Install

Overview

Description from the maker (original English)

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

PopNx MCP’s three pillars

These three are never merged into one score: opinions, measurements and popularity are different things.

What you can do with it

We have not written a plain-language summary for this one yet. Below are the facts we read from the registry.

  • Connection: Runs on your computer
  • Transport: stdio
  • Category: Clinics & health

Install

Pick the AI app you use, then copy the settings.

Before you install

  • Basic checks: 1 of 1 passed
  • Runs on your computer with your user’s permissions (files, network, apps).
  • The registry lists no key to fill in.

You need

  • PyPIPython’s uv installed (for the uvx command)

Which app do you use?

Pick your app above. We remember it, so next time the button says “Install in …”.

Open Claude Desktop → Settings → Developer → Edit Config, and paste this into claude_desktop_config.json

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

Run this in your terminal

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

Open in Cursor

Or copy the settings yourself

Paste into .cursor/mcp.json in your project, or ~/.cursor/mcp.json

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

Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.

Open in VS Code

Or copy the settings yourself

Paste into .vscode/mcp.json in your project

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

Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.

This one runs on your computer, and ChatGPT can only connect to servers on the internet. Pick another app.

Your choice stays on this device in a cookie; no account needed.

These settings are generated from the maker’s registry entry. If the README says otherwise, follow the README.

What it can do

Runs on your computer; the tool list appears in your AI app after you install it.

Basic checksi

We check information anyone can see; we have not read the code. Checks that do not apply are left out of the count. · Checked Nov 8, 2026

  • ✓ Pass

    Maker matches the repo

    The registry name was verified with the GitHub account jaimenbell, which owns the repo

  • … Not checked yet

    Still maintained

    We have not read this repo from GitHub yet

  • … Not checked yet

    Has a licence

    We have not read this repo from GitHub yet

  • – Not applicable

    Endpoint responds

    This one runs on your computer; there is no internet endpoint

  • – Not applicable

    Sign-in with OAuth

    Runs on your computer and uses local settings instead

  • … Not checked yet

    No hidden instructions in tool descriptions

    Runs on your computer; we can only read tool lists from internet endpoints

Compared with others in Clinics & healthi

  • More stars than 0% of the other 1028 servers in this category
  • Published to the registry more recently than 23% of the other 1028 servers in this category
  • Basic checks equal to or better than 100% of the other 1028 servers in this category

Compared with every active server in this category in the registry. Stars count only when the publisher owns the repo.

Common questions

Is Vllm Ops MCP safe?

It passed 1 of 1 applicable basic checks. We have not read the code. Before adding a key or token, see what access the server asks for and use a key with only the permissions you need.

What keys or codes does it need?

The registry does not list a required key, though some servers ask you to sign in on first use.

Does it run on my computer or in the cloud?

It runs on your computer. Your AI app starts it when needed, and data does not pass through the maker’s servers (unless it calls an online service itself).

Is it still maintained?

The GitHub repo last changed –, and the latest version (0.1.0) reached the registry 4 months ago.

Which apps can use it?

Apps that support MCP, such as Claude Desktop, Claude Code, Cursor and VS Code. You also need Python (uv) on your computer.

What can I use instead?

In the same category: Mirobody, FHIR MCP Server, Google Health MCP

Alternatives

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Runs on your computer✓ thetahealth.ai

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Runs on your computer

README from the maker

Open the README

Copied from the maker’s repo as written. PopNx MCP has not reviewed or endorsed it. HTML, images and scripts are removed. Links lead off this site.

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

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