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5 days ago
glama

RagDocs MCP Server

Enables semantic search and management of documentation through vector similarity using Qdrant and Ollama/OpenAI embeddings.

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// key findings
⚠️
Known vulnerabilities in dependencies: 17 high
Affects packages this MCP installs at runtime. Upgrade or remove the affected dependency.
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.
🔐
You'll be asked for 2 credentials: OPENAI_API_KEY, QDRANT_API_KEY
These are read from process.env at runtime. Make sure you trust where they’ll be sent.
// required environment variables
This server reads these from process.env. You'll be asked to provide them before it can run.
configEMBEDDING_PROVIDER"": "ollama"
🔐 secretOPENAI_API_KEY"": "your-api-key"
🔐 secretQDRANT_API_KEY"": "your-qdrant-api-key",
configQDRANT_URL"": "http://127.0.0.1:6333",
// full audit trail
The full breakdown of what we checked, the deductions that landed, the network hosts, the dependency advisories, and concrete fix guidance is available to verified publishers.
// improvement guidance — verified publishers only
We have 6 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/mcpflow-ragdocs-8cdt8o)](https://m8ven.ai/mcp/mcpflow-ragdocs-8cdt8o)
commit: 49101ee600ec9365becd8efb4d97e92edf88f439
code hash: 14118d82782c215a7e02fa0db404c64a5c3699088dc53473e37f5292515d6c15
verified: 6/16/2026, 1:24:29 PM
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