Screeny (rohanrav/screeny) is an MCP server listed on the M8ven Trust Index. It scores 41 out of 100, grade D. It declares 2 tools. No publisher has claimed this listing.

D
Caution
41/100

Screeny

Screeny is a privacy-focused macOS MCP server enabling AI agents to capture screenshots of user-approved windows

Caution. Specific findings reduced this grade. They are listed on the page. Grades reflect the full trust pyramid: code, verification depth, and reputation. New projects cap at C until adoption is earned.

How we verified

⚡ Live Monitored: not connected

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⚡ Connect GitHub → continuous verification on every pushwhy connect →

Who stands behind it

rohanrav

Source: mcp.so

Is this your MCP?

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Install from

The grade above is for the source repository. Registries can serve a different version, so we mark the ones we were not able to read.

// key findings
⚠️
Tool annotations don’t match behaviour
1 read-only tool performs write/delete/exec — take_screenshot (line 619: os.unlink(tmp_path))
No credential exfiltration, no sensitive file access, no obfuscation
Static analysis found nothing flowing your secrets to unexpected places.
Open source with a license and README
Anyone can audit the code, the license is declared, and the publisher documents what it does.
// tools this server exposes2 tools

These names and descriptions are the publisher's own, read from the source code. We print them as written. Our assessment is the findings above, not this list.

list_windows

List all currently approved windows available for screenshot capture.

take_screenshot

Take a screenshot of a specific window by its ID using direct capture.

// environment variables
To run this server yourself, you supply these values. They go in your own MCP client configuration and stay on your machine. The secret label means the value is sensitive, not that the server mishandles it.
configSCREENY_MAX_B64_KBYour choice is saved in ~/.screeny/config.json as max_b64_kb. You can also override via the environment variable. The active cap is clamped to 100–900KB.
// quality suggestions

All four hints declared on every tool

2/2 tools missing one or more hints — list_windows (missing: destructiveHint, idempotentHint); take_screenshot (missing: destructiveHint, idempotentHint). OpenAI's directory rejects tools where any of the four hints are missing or non-boolean.

For every tool, set all four hints (readOnlyHint, destructiveHint, idempotentHint, openWorldHint) to explicit true/false values that match the handler’s actual behaviour.

Tool annotations match behaviour

1 read-only tool performs write/delete/exec — take_screenshot (line 619: os.unlink(tmp_path))

Either remove the readOnlyHint:true annotation, or remove the write/delete call from the tool handler.

Destructive tools are labelled

1 tool perform destructive updates without destructiveHint — take_screenshot deletes at line 619 (os.unlink(tmp_path))

Add destructiveHint:true to any tool whose handler calls .delete(), .upsert(), .update(), unlink, rm, DELETE, DROP, REPLACE INTO, or any operation that overwrites existing data.

Tool inputs are validated

1/2 tool handlers declare input schemas (50%)

Declare an inputSchema with zod/joi/yup on every tool definition.

Tests exist

No test files found

Add tests that exercise each declared tool.

Claim the listing to review these findings one by one and send us a correction where you disagree, straight to the team. Claiming also means we tell you when the grade moves, and reach you first if we find anything urgent.

// full audit trail
The findings above are the summary. The full trail, every check we ran, each deduction, the network hosts observed and the dependency advisories, goes to verified publishers, along with an alert whenever a new one lands. Verified publishers can also review each finding and dispute it in one click. Publisher corrections have sharpened several of our checks this month, because the maintainer knows the codebase better than any scanner.
// improvement guidance — verified publishers only
We have 5 concrete improvements we can share with the publisher of this MCP. Each comes with specific guidance to raise the trust score.
// embed badge in your README
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commit: 99c7ddd1234d6f13b74343cd9d980af7228d9bea
code hash: dca75c327fd94dfd73ca68779d18821df12b00cb9428c7ba62daa34a219aebe7
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