mcp-local-rag (xiuxiansk/mcp-local-rag) 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
Emerging
74/100
23 days ago

mcp-local-rag

MCP server that enables local hybrid semantic and keyword search over private PDF, DOCX, Markdown, and text documents without sending data to embedding APIs.

Emerging. No concerning findings. Grades remain capped until the project builds reputation through adoption. Grades reflect the full trust pyramid: code, verification depth, and reputation. New projects cap at C until adoption is earned.

How we verified

Code Verified⚡ Live Monitored: not connected

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Who stands behind it

xiuxiansk

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.
Open source with a license and README
Anyone can audit the code, the license is declared, and the publisher documents what it does.
// 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.
configBASE_DIRSet to that directory. It is also the security boundary for file operations. Replace
configBASE_DIRS(unset) JSON array of document roots; takes precedence over BASE_DIR
configCACHE_DIRcache-dir ./models/ Model cache directory
configCHUNK_MIN_LENGTHchunk-min-length 50 Minimum chunk length in characters (1–10000)
configCODEX_HOME
configDB_PATHsame directory so they use the same default index, or set BASE_DIR and explicitly.
configHF_MODEL_REVISION
configMAX_FILE_SIZEmax-file-size 104857600 (100MB) Maximum file size in bytes
configMODEL_NAMEboth interfaces should share an index. In particular, and the CLI --model-name
configRAG_BATCH_SIZE
configRAG_DEVICEcpu ONNX Runtime execution device
configRAG_DTYPEfp32 Embedding dtype supplied by the selected model
// 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

18/18 tools missing one or more hints — query_documents (missing: readOnlyHint, destructiveHint, idempotentHint, openWorldHint); ingest_file (missing: readOnlyHint, destructiveHint, idempotentHint, openWorldHint); ingest_data (missing: readOnlyHint, destructiveHint, idempotentHint, openWorldHint), +15 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 test coverage

6/18 tools referenced in tests (33%)

Write tests that reference each tool by name so every tool has at least one test.

Shell command execution

2 child_process calls — runs shell commands

Prefer library functions over shell-outs. If you must shell out, ensure all inputs are properly escaped.

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 4 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
[![M8ven Score](https://m8ven.ai/badge/mcp/xiuxiansk-mcp-local-rag-1warpl)](https://m8ven.ai/mcp/xiuxiansk-mcp-local-rag-1warpl)
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commit: a1f025a823eee81f2d95b80fea7fc0997a6fa239
code hash: d05de881ddee3539f0fae7dab0112c76da47e02a02f0f07815f8d41c73b6c227
verified: 8/7/2026, 7:49:44 AM
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