Luma (AceDataCloud) MCP Server

mcp-luma PyPI v2026.8.28.0

Published by acedatacloud — no publish provenance, so origin is unverified, but the source is public: the repository link below is self-declared yet readable, so you can inspect the code before adopting it.

MCP Server for Luma AI Video Generation via AceDataCloud API

View on PyPI ↗ Repository ↗ Raw report (JSON) ↗

Trust grade

99/100

Last scanned 2026-08-29 get badge →

Trust

A · 99/100

Adoption risk for you: the threat score, then adjusted down for blast radius, publisher verification and how much the scan could see. Deterministic; every point is auditable.

Capability

Minimal

Blast radius if it went rogue — what the server’s tools could reach. Independent of trust.

Coverage

Source

How much the scan could actually inspect. Shallow coverage is stated, never hidden.

A Why this grade threat 100 − adoption risk = 99/100

The grade answers one question — how safe is this server for you to adopt — so it is computed in two auditable stages. Nothing below is an opinion or an LLM's guess; every line is a real term the deterministic engine applied, and the same input always yields the same number.

1. Threat score — 100 − 0 = 100. What the published surface and source actually contain:

The deterministic scan raised no scored threat in the surface it inspected — the threat score stayed at 100. Capability observations and advisory notes are recorded but never lower it.

2. Client adoption risk — 100 − 1 = 99. Three small, subtract-only factors that reflect your risk in adopting it — a clean scan proves less on a powerful, unverified or barely-inspectable package, so the grade says so plainly:

Points	Adoption-risk factor
−1	publisher verification (public source) — no provenance, but the source is public and inspectable

Capability observations and info notes are shown under Findings but never scored. Open any row's finding below for the file, line and evidence behind a deduction.

Findings 0

✓ No findings. The scan raised nothing on this surface — see Coverage for how deep it could look.

Tools 8

Each tool and what it can reach — statically extracted from the published source.

luma_extend_videono sensitive capability
luma_extend_video_from_urlno sensitive capability
luma_generate_videono sensitive capability
luma_generate_video_from_imageno sensitive capability
luma_get_taskno sensitive capability
luma_get_tasks_batchno sensitive capability
luma_list_actionsno sensitive capability
luma_list_aspect_ratiosno sensitive capability

What this scan could not see

Tools were statically extracted from the published source (8 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>".

Versions 8

Scan history per published version. The engine is deterministic — the same version always yields the same score, so a changed score means the package itself changed.

Version	Score	Engine	Scanned
`v2026.8.28.0` latest	A 99/100	1.13.0	2026-08-29
`v2026.8.23.0`	A 99/100	1.13.0	2026-08-25
`v2026.8.18.0`	A 99/100	1.12.1	2026-08-19
`v2026.8.17.0`	A 99/100	1.12.1	2026-08-18
`v2026.8.15.0`	A 99/100	1.12.1	2026-08-16

Show 3 more versions ↓

`v2026.8.10.0`	A 99/100	1.12.1	2026-08-10
`v2026.7.30.0`	A 99/100	1.12.1	2026-07-31
`v2026.7.4.0`	A 99/100	1.12.1	2026-07-27

Embed this score

Show this server's live Trust Score in your README, docs or website. The badge is served straight from the registry and updates automatically after every rescan — no API key needed. It links back to this page, so anyone who sees the grade can also read the findings behind it instead of taking a number on faith.

Markdown (GitHub README)

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HTML

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Verify this score yourself

The score above is reproducible: the same package version always yields the same result. Run it locally or over the free API — no account, no LLM, fully deterministic.

npx mcptrustchecker scan mcp-luma --online --registry pypi

Use the free API → How scoring works

Other implementations of Luma 2

Independent packages implementing the same tool, scanned with the same engine. Compare all 3 side by side →

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