query-sanitizer-mcp (vidoluco/query-sanitizer-mcp) is an MCP server listed on the M8ven Trust Index. M8ven has not graded it: we have no way to read this server ourselves. No publisher has claimed this listing.

C
Limited view
69/100
2 months ago

query-sanitizer-mcp

A local DLP middleware that redacts sensitive information from prompts using local models before they reach external LLMs. It provides tools to sanitize queries, restore placeholders in responses, and manage a ledger of redactions to maintain data privacy.

Limited view. Automated analysis covers part of this stack. Findings reflect what we verified. Grades reflect the full trust pyramid: code, verification depth, and reputation. New projects cap at C until adoption is earned.

Limited view: static analysis for Python is partially covered.

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Who stands behind it

vidoluco

Source: Glama

Is this your MCP?

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// key findings
No credential exfiltration, no sensitive file access, no obfuscation
Static analysis found nothing flowing your secrets to unexpected places.
// 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.
configSANITIZER_LEDGER_DIRos.environ[""] = "/content/sanitizer-ledger"
configSANITIZER_MODEL_URL
configSANITIZER_MODEL_NAME"": "qwen2.5:3b",
configSANITIZER_LEDGER_STORE_ORIGINALStrue Set to false to stop storing original values at rest (GDPR mode — restore only works within the same session)
configSANITIZER_MODEL_RETRIES0 python server.py
configSANITIZER_BACKENDos.environ[""] = "hf"
configSANITIZER_HF_MODELos.environ[""] = "Qwen/Qwen2.5-3B-Instruct" # ~6GB, fits T4 16GB
configSANITIZER_GLINER_MODEL"": "urchade/gliner_medium-v2.1"
configSANITIZER_GLINER_THRESHOLD
configSANITIZER_HF_DTYPEauto HF pipeline dtype. float16 halves LLM RAM on the HF backend. Warning: CPU float16 may fail on some Windows torch builds — test before setting.
configSANITIZER_SESSION_CACHE_MAXSame as M4 setup but add =100 to cap RAM growth:
// quality suggestions

Tool annotations

No tools have read-only/destructive annotations

Add readOnlyHint or destructiveHint annotations to every tool so hosts can warn users before invoking.

All four hints declared on every tool

4/4 tools missing one or more hints — sanitize_query (missing: readOnlyHint, destructiveHint, idempotentHint, openWorldHint); restore_response (missing: readOnlyHint, destructiveHint, idempotentHint, openWorldHint); scan_response (missing: readOnlyHint, destructiveHint, idempotentHint, openWorldHint), +1 more. 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 handlers catch errors

2/4 tool handlers wrap calls in try/catch (50%)

Wrap each tool handler body in try/catch and return a structured error response.

License file

No license file

Add a LICENSE file (MIT, Apache-2.0, etc.).

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.
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commit: d8ab17a35241db35ce41139e1d84fcdbb4684327
code hash: 1f8d5cc6e6db0fa64fe40c44e41a7402f942550e2d1700b831850dc5cb129eab
verified: 6/16/2026, 11:34:24 AM
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