An MCP server that connects to Vercel API
Nothing to run from here
CloudGPU is installed from its publisher's own source and answers where it runs, so this marketplace is not in the path of a single call. There is no address here to send one to, and a panel that pretended otherwise would be showing you an answer we made up. Install it and call it from your own client — the Installation tab has the entry for each one.
94/100
trust score
10
downloads a day
local
runs on your machine
606
downloads in 30d
What it does
Rent a per-minute GPU from your MCP client, run a job, download results, shut it down.
Quickstart
# 1 — add it to Claude Code
claude mcp add --scope user --env 'CLOUDGPU_API_KEY=${CLOUDGPU_API_KEY}' --transport stdio cloudgpu-mcp '--' npx -y cloudgpu-mcp
# 2 — ask your agent for something these tools do
# get_account, list_gpus, list_templates and 10 more
It needs CLOUDGPU_API_KEY: this reads it from your environment, so export it first.
CloudGPU costs nothing on mcprush: there is no plan to choose here, no cap to set and nothing mcprush can bill you for.
Publishernot claimed
MRAll 1 server
mrhy71761 server
1 category
Collected from a public index. Nobody has claimed this account, so nothing here was written by its author — claim it if it is yours.
Installation
Where are you running it?
Every route below installs the same thing and ends at the same approval screen. This one runs on your machine: your client starts CloudGPU as a process under your own user, with your files and your network, so the tool surface below is what it can do to your computer rather than to a server somewhere else. Its scan result is on the Security tab — read the surface before you approve it.
Claude Codedeclared by this listing
This is a public server: you run it yourself and this marketplace is not in the path. You need Node.js — the current LTS (24) is the safe choice; npx comes with it. Claude Code registers it in one command; --scope user makes it available in every project. Claude Code reads CLOUDGPU_API_KEY (CloudGPU API key (starts with cgw-sk-). Create one at https://cloudgpu.app/api) from your environment each time it starts the server, so the value stays out of its config: export it in your shell profile (macOS, Linux), or run setx CLOUDGPU_API_KEY … and open a new terminal (Windows). The '--' is in quotes so that PowerShell passes it on.
claude mcp add --scope user --env 'CLOUDGPU_API_KEY=${CLOUDGPU_API_KEY}' --transport stdio cloudgpu-mcp '--' npx -y cloudgpu-mcpReconnect, or start a new session, and the tools appear in the model’s tool list.
Free on mcprush, and it runs from its publisher’s own package: this marketplace is not in the path, so there is no key of ours to hold and no card to add.
What it asks of you
It reaches a system of yours, so it needs your own credential rather than the publisher’s: CLOUDGPU_API_KEY — CloudGPU API key (starts with cgw-sk-). Create one at https://cloudgpu.app/api. You set it in your own client’s config, on your machine — this marketplace never holds it.
What it writes
One config entry your client uses to start the process locally. A local server runs with your file system and your network, which is why it is priced without metering.
What you approve
13 tools, with what each one reads, writes and reaches shown before you agree — the same list on every route above. Read the tool surface.
Tool surface
What the model actually sees. Descriptions are diffed on every release — see version history.
Read from its source code. A server that builds its tool list when it starts can list more tools than this.
Balance in USD, how many instances this account may run at once, and how many are running now.
No parameter schema on file.
GPU models you can rent now, with VRAM, live price per GPU-hour in USD and availability (ok / low / none). Prices are live; never assume them.
| Parameter | Type | Required | Notes |
|---|---|---|---|
| min_vram_gb | number | no | Only GPUs with at least this much VRAM per card |
| gpu_model | string | no | Filter by name, e.g. 4090, A100, H20 |
Ready-made instance images (e.g. JupyterLab, Ollama, ComfyUI, LLaMA-Factory) with the GPU each recommends. Templates with has_api_endpoint get a public HTTPS api_url.
No parameter schema on file.
Live price and estimated cost for a GPU, plus your balance after it. Returns a quote_id that create_instance needs. Show the quote to the user and get an explicit yes before renting.
| Parameter | Type | Required | Notes |
|---|---|---|---|
| gpu_model | string | yes | GPU model exactly as list_gpus returns it |
| gpu_count | integer | no | — |
| hours | number | yes | Expected run time in hours |
| template | string | no | Template id from list_templates; omit for a plain GPU with Python and CUDA |
Models on CloudGPU's OpenAI-compatible API (same key, same balance). Use them from code with base_url
No parameter schema on file.
Status (creating / running / stopped / failed), minutes run, cost so far, whether SSH is ready, the public api_url if the template has one, and the auto-shutdown limits.
| Parameter | Type | Required | Notes |
|---|---|---|---|
| instance_id | string | yes | — |
Instances on this account: running, starting, and those stopped in the last 7 days, with cost.
No parameter schema on file.
Rents the GPU from a quote. Costs money. Requires a quote_id from estimate_cost that the user has approved.
| Parameter | Type | Required | Notes |
|---|---|---|---|
| quote_id | string | yes | — |
| user_confirmed | boolean | yes | true only if the user explicitly approved this quote in the conversation |
| max_spend_usd | number | yes | Budget for this instance in USD (at most …) |
| max_hours | number | yes | Hard time limit in hours (at most …) |
| idle_minutes | integer | no | Destroy after the GPU has been idle this long; 0 turns it off; otherwise at least 10 |
Runs a bash command on the instance as root, in an interactive-style shell so conda/venv from ~/.bashrc are active.
| Parameter | Type | Required | Notes |
|---|---|---|---|
| instance_id | string | yes | — |
| command | string | yes | bash command or script |
| background | boolean | no | — |
| timeout_sec | integer | no | Foreground only |
| workdir | string | no | Directory to run in |
Copies a local file or directory (this computer) to the instance over SFTP. node_modules, .git and __pycache__ are skipped. Credentials files (.ssh, .env, keys) are refused unless allow_sensitive is true and the user asked for it.
| Parameter | Type | Required | Notes |
|---|---|---|---|
| instance_id | string | yes | — |
| local_path | string | yes | — |
| remote_path | string | yes | Destination on the instance, e.g. /root/project or /root/data/ |
| allow_sensitive | boolean | no | — |
Copies a file or directory from the instance to this computer. Do this for every result before stop_instance: destroying an instance erases its disk.
| Parameter | Type | Required | Notes |
|---|---|---|---|
| instance_id | string | yes | — |
| remote_path | string | yes | — |
| local_path | string | yes | Local destination file or directory, e.g. ./outputs/ |
Last lines of a background job's log (job_id) or any log file (path), whether the job is still running and its exit code, plus current GPU utilisation and memory.
| Parameter | Type | Required | Notes |
|---|---|---|---|
| instance_id | string | yes | — |
| job_id | string | no | — |
| path | string | no | — |
| lines | integer | no | — |
Destroys the instance and stops billing. Its disk is erased. If commands were run on it in this session and nothing was downloaded, this refuses unless force=true (only after the user confirms the results are not needed).
| Parameter | Type | Required | Notes |
|---|---|---|---|
| instance_id | string | yes | — |
| force | boolean | no | — |
Free
$0 forever
- Every tool, no call limit
- No card, and mcprush charges nothing for it
- Source published under MIT
- Runs on your machine — nothing of it reaches our gateway
- Nothing to cap, because mcprush bills nothing
What counts against your monthly calls
| Tool | Unit | Calls used | Out of the allowance |
|---|
Nothing here is billable: mcprush charges nothing to install CloudGPU or to call it, at any volume.
Call allowance · this installenforced at the gateway
Monthly allowance, in calls
—
—
Alert at 80% · then the tool returns a normal MCP error
Used this month: —
ATrust score 94 / 100
Release 0.1.2 · scanned 2 Nov 2026
Two independent axes, because powerful and malicious are different questions. The grade is threat only. The capability level is blast radius, and it is never a penalty on the grade — it is priced as one subtract-only term in the score, where you can see it.
Threat score100
Client score94
Findings0 threat · 3 capability
Capabilityhigh
Coveragesource
Verificationsource
Surface digest6bc818c060558c889e3c2f861a461e61ff120625fc06c4b3e4bc618056023710
Score vectorthreat 100 → client 94
| Term | Level | What it prices | Points |
|---|---|---|---|
| capability-exposure | high | capability blast radius (high) — client exposure if the model is manipulated | −6 |
| verification-discount | source | publisher verification (provenance) — cryptographic build provenance ties the artifact to its source | −0 |
| coverage-honesty | source | inspection depth (source) — how much of the target the scan could see | −0 |
score = clamp(round(threatScore − E_cap − E_ver − E_cov), 0, 100). No model, no opinion, no network call at scoring time: the same methodology version against the same bytes always produces this number, and every line above is a field of the report rather than a summary of it.
What the scan could actually read
A grade is only as meaningful as its coverage, so the scanner publishes its own depth before it publishes its result.
Tool surfaceread
Implementation sourceread
Package metadataread
Live transportnot available
Tools were statically extracted from the published source (13 recovered), not enumerated from a running server. Tool-poisoning, Unicode-smuggling, capability and toxic-flow analysis ran on this inferred surface, but a mis-parsed registration could be missed or mis-attributed, so tool-derived findings are capped below “confirmed”. To grade the real runtime surface, scan the running server: --command "npx -y <package>".
6 of the 13 recovered tools are registered only when !cfg.readOnly holds (src/index.js:278) — typically when a key or option is configured. They are part of the server's surface and were analysed; a server started without that configuration exposes only the other 7.
Capability — what it could do if the model were manipulated
Tags derived from each tool’s schema and the implementation, not from what the tool calls itself. high is the level these add up to.
7 tools are registered unconditionally · 6 more only when !cfg.readOnly holds (src/index.js:278) — typically when a key or option is configured. All 13 are in the table below and were analysed; the conditional ones are marked.
| Tool | Capability tags | Why the tag was assigned |
|---|---|---|
| get_account | untrusted-input | untrusted-input: declares openWorldHint: true (interacts with entities outside the server) |
| list_gpus | untrusted-input | untrusted-input: declares openWorldHint: true (interacts with entities outside the server) |
| list_templates | untrusted-input | untrusted-input: declares openWorldHint: true (interacts with entities outside the server) |
| estimate_cost | untrusted-input | untrusted-input: declares openWorldHint: true (interacts with entities outside the server) |
| list_models | untrusted-input | untrusted-input: declares openWorldHint: true (interacts with entities outside the server) |
| get_status | untrusted-input | untrusted-input: declares openWorldHint: true (interacts with entities outside the server) |
| list_instances | untrusted-input | untrusted-input: declares openWorldHint: true (interacts with entities outside the server) |
| create_instance conditional | external-sink | external-sink: declares readOnlyHint: false, destructiveHint: true, openWorldHint: true |
| run_command conditional | code-execexternal-sink | code-exec: keyword "run_command" in tool name, parameter "command"; external-sink: declares readOnlyHint: false, destructiveHint: true, openWorldHint: true |
| upload conditional | external-sink | external-sink: declares readOnlyHint: false, openWorldHint: true |
| download conditional | untrusted-inputexternal-sink | untrusted-input: keyword "download"; external-sink: declares readOnlyHint: false, openWorldHint: true |
| tail_logs conditional | untrusted-input | untrusted-input: declares openWorldHint: true (interacts with entities outside the server) |
| stop_instance conditional | external-sink | external-sink: declares readOnlyHint: false, destructiveHint: true, openWorldHint: true |
Toxic-flow graph
The lethal trifecta, checked as a graph rather than as a checklist: untrusted input, a sensitive source and an external sink have to meet before there is a path worth worrying about.
flow-1
Untrusted input can drive an external action even though no sensitive source is exposed.
Supply chain and provenance
Integrityfirst-seen
Surface digest6bc818c060558c88
Scanned2026-11-02
This is the first scan of this surface here, so there is nothing yet to compare it against.
Every result on this tab comes from one deterministic pass over the published package — offline, rule by rule, and auditable line by line above. Same methodology version, same bytes, same score.
Release history
Always latest — the install command names no version, so it installs the newest release when it runs; the current release on file is 0.1.2. Pin a release below and the command asks for exactly that one.
0.1.2
2 Nov 2026
latestNo release note was published with this version.
0.1.1
28 Oct 2026
No release note was published with this version.
0.1.0
undated
- The version this listing was on when mcprush began following its releases. The registry was not asked when it shipped, so this row carries no date.
—
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One review per account, from a signed-in account that does not publish this listing. Nothing else is asked — you can say what you think before you install it. Publishers can reply once per review, and a review can be edited or deleted by whoever wrote it.
Sign in to write one
Any signed-in account that does not publish CloudGPU can review it, once — before installing it or after. A review from an account that has installed it is marked as one.
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Nobody has reviewed this listing. The rating on the card is the mean of the reviews written here and nothing else, so there is no rating until somebody writes the first — which takes a signed-in account that does not publish it, and nothing else.