What is mcp-ragdocs?

61/100
Trust Score (C)
⚠️ Use Caution

mcp-ragdocs is a infrastructure that An MCP server implementation that provides tools for retrieving and processing documentation through vector search, enabling AI assistants to augment their responses with relevant documentation contex. It has a Nerq Trust Score of 61/100 (C). 249 GitHub stars. Published by hannesrudolph. Last analyzed April 2026.

Why This Score

Trust & Safety Overview

61
TRUST SCORE
C
GRADE
249
STARS
0
DOWNLOADS

What mcp-ragdocs Does

mcp-ragdocs is a mcp_server in the infrastructure category. An MCP server implementation that provides tools for retrieving and processing documentation through vector search, enabling AI assistants to augment their responses with relevant documentation context.. It is published by hannesrudolph and has no specified license. With 249 GitHub stars and 0 downloads, it has a growing community of users and contributors.

Who Should Use mcp-ragdocs

mcp-ragdocs is suitable for evaluation and non-critical use. Review the trust score breakdown before using in production.

Details

Authorhannesrudolph
Categoryinfrastructure
LicenseNot specified
Typemcp_server
SourceView on GitHub
Security Score0/100
Activity Score0/100

How to Get Started

Check the trust score before installing:

curl nerq.ai/v1/preflight?target=mcp-ragdocs

Setup guide · Full safety report · Production review · Is it safe?

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Frequently Asked Questions

What is mcp-ragdocs used for?
mcp-ragdocs is a infrastructure tool. An MCP server implementation that provides tools for retrieving and processing documentation through vector search, enabling AI assistants to augment their responses with relevant documentation contex.
Is mcp-ragdocs free?
License: Check project page. mcp-ragdocs has 249 GitHub stars.
Is mcp-ragdocs safe?
mcp-ragdocs has a Nerq Trust Score of 61/100 (C). Use with caution.
What are alternatives to mcp-ragdocs?
Top alternatives: n8n, langflow, dify. See full comparison.

Last updated April 2026. Trust scores based on automated analysis of public data.

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