Ascript MCP Server
ascript-mcp
PyPI
v1.7.0
Published by an unidentified publisher — no publish provenance and no public repository, so the publisher could not be verified and the source cannot be independently located.
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 − 8 = 92. 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 |
|---|---|
| −6 | capability blast radius (high) — client exposure if the model is manipulated |
| −2 | publisher verification (unlinked) — no provenance/repo link, but the shipped source was fully read |
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 5
high Sensitive-source and external-sink co-existMTC-FLOW-004
Tools that read sensitive data ([dump_ui_tree]) and tools that can send data out ([eval_python, upload_file]) are exposed together. An agent can move private data to the sink.
Evidence: sources [dump_ui_tree] → sinks [eval_python, upload_file]
Fix: Keep secret-reading and egress capabilities on separate, separately-approved servers.
Location: flow dump_ui_tree → eval_python
high Tool "eval_python" exposes command/code executionMTC-CAP-001
Tool "eval_python" appears to run shell commands or evaluate code (keyword "eval" in tool name). Arbitrary execution driven by model input is one of the most dangerous MCP capabilities; combined with any untrusted input it becomes RCE.
Fix: Sandbox execution, allowlist commands/arguments, and never pass model output to a shell unescaped.
Location: tool eval_python
high Shell/command execution in server code (src/ascript_mcp/device.py)MTC-SRC-002
In the server's implementation (`src/ascript_mcp/device.py:531`): Spawning a shell/process is command-execution capability; with unsanitized tool input it is command injection / RCE. This is read from the code itself — not from the tool description — so a poisoned server cannot hide it behind honest-looking metadata.
Evidence: try: result = subprocess.run( [adb, "devices"], capture_output=True, text=
Fix: Review this call path: confirm it never receives unsanitized tool input, constrain it, or remove it. Treat a server whose code reaches these sinks as high-capability regardless of what its tools claim.
Location: server src/ascript_mcp/device.py
medium Unconstrained command parameter "code" on "eval_python"MTC-CAP-006
Tool "eval_python" takes a command-shaped parameter "code" with no enum/pattern constraint. Free-form, model- or attacker-controlled arguments reaching a shell is the command-injection precondition.
Fix: Constrain the parameter (enum/pattern), or build the command from a fixed template with escaped args.
Location: tool eval_python · inputSchema.properties.code
low Mutating tool "eval_python" declares no destructiveHintMTC-CAP-005
Tool "eval_python" can mutate/egress but declares no destructiveHint. Clients that don't default to spec-safe behavior may not prompt before running it.
Fix: Declare accurate annotations, and gate destructive tools on user confirmation regardless.
Location: tool eval_python
Tools 29
Each tool and what it can reach — statically extracted from the published source.
dump_ui_treereads sensitive dataeval_pythonruns code / shellupload_filenetwork egressauto_connectno sensitive capabilitycompare_colorsno sensitive capabilityconnect_deviceno sensitive capabilitycreate_projectno sensitive capabilitydeploy_and_runno sensitive capabilityfind_colorsno sensitive capabilityget_code_exampleno sensitive capability
Show 19 more tools ↓
get_device_statusno sensitive capabilityget_module_apisno sensitive capabilityget_platform_overviewno sensitive capabilityget_plugin_detailno sensitive capabilityget_project_filesno sensitive capabilityget_run_logno sensitive capabilityget_setup_guideno sensitive capabilitylist_pluginsno sensitive capabilitylist_projectsno sensitive capabilitylist_python_packagesno sensitive capabilityobserve_deviceno sensitive capabilityocrno sensitive capabilityrun_projectno sensitive capabilityrun_project_debugno sensitive capabilityscan_devicesno sensitive capabilityscreen_captureno sensitive capabilitysearch_apino sensitive capabilitystop_projectno sensitive capabilitytest_selectorno sensitive capability
Toxic flows 1
Cross-tool combinations that form a data-exfiltration primitive (untrusted input → sensitive source → external sink).
What this scan could not see
- Tools were statically extracted from the published source (29 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 1
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 | Findings | Engine | Scanned |
|---|---|---|---|---|
v1.7.0 latest |
A 92/100 | 5 | 1.9.0 | 2026-07-23 |
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.
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.
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