MCP-RAGNAR (bixentemal/mcp-ragnar) is an MCP server listed on the M8ven Trust Index. It scores 74 out of 100, grade C. It declares 2 tools. No publisher has claimed this listing.

Back to MCP Trust IndexM8ven
C
Emerging
74/100

MCP-RAGNAR

A local RAG server that enables document indexing and sentence window retrieval across multiple file formats like PDF, MD, and DOCX. It supports both local Hugging Face models and OpenAI embeddings for efficient context-aware querying through the Model Context Protocol.

→ bixentemal/mcp-ragnar· listed on glama

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.

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Who stands behind it

bixentemal

Source: Glama

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Install from

Glamanot scored here

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
✅
No credential exfiltration, no sensitive file access, no obfuscation
Static analysis found nothing flowing your secrets to unexpected places.
🔐
You'll be asked for 1 credential: OPENAI_API_KEY
These are read from process.env at runtime. Make sure you trust where they’ll be sent.
// 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.
configINDEX_ROOTThe root directory for the index, used by the retriever. This is mandatory for MCP (Multi-Cloud Platform) querying.
configMCP_DESCRIPTIONThe exposed name and description for the MCP server, used for MCP querying only. This is mandatory for MCP querying. For example: "RAG to my local personal documents"
🔐 secretOPENAI_API_KEY
configEMBED_ENDPOINT(Optional) Path to an OpenAI compatible embedding endpoint (ends with /v1). If not set, a local Hugging Face model is used by default.
configEMBED_MODEL(Optional) Name of the embedding model to use. Default value of BAAI/bge-large-en-v1.5.
// 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

2/2 tools missing one or more hints — handle_call_tool (missing: readOnlyHint, destructiveHint, idempotentHint, openWorldHint); retrieve (missing: readOnlyHint, destructiveHint, idempotentHint, openWorldHint). 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.

License file

No license file

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

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 3 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/bixentemal/mcp-ragnar)](https://m8ven.ai/mcp/bixentemal/mcp-ragnar)
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commit: 6e39b67a6da61940ee0fa2db70ee4e7a1b766864
code hash: ad72cc939e2b3d59dc8d26e9151f01a3e26d8a9c812ccfc7a35c53e381e89421
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