What is TeaRAGs-MCP?

68/100
Trust Score (C)
⚠️ Use Caution

TeaRAGs-MCP is a devops that A high-performance MCP server for intelligent codebase analysis. Enterprise-ready: handles millions of LOC with fast local indexing. Semantic code search, git-aware metadata (authorship, churn, task I. It has a Nerq Trust Score of 68/100 (C). 10 GitHub stars. Published by artk0de. Last analyzed September 2026.

Why This Score

Trust & Safety Overview

68
TRUST SCORE
C
GRADE
10
STARS
0
DOWNLOADS

What TeaRAGs-MCP Does

TeaRAGs-MCP is a mcp_server in the devops category. A high-performance MCP server for intelligent codebase analysis. Enterprise-ready: handles millions of LOC with fast local indexing. Semantic code search, git-aware metadata (authorship, churn, task IDs), with graph indexing and symbol analysis coming soon. Built on Qdrant. Works with Ollama (local,. It is published by artk0de and is open source. With 10 GitHub stars and 0 downloads, it has a small community of users and contributors.

Who Should Use TeaRAGs-MCP

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

Details

Authorartk0de
Categorydevops
LicenseMIT
Typemcp_server
SourceView on GitHub
Security Score0/100
Activity Score1/100

How to Get Started

Check the trust score before installing:

curl nerq.ai/v1/preflight?target=artk0de-tearags-mcp

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

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Frequently Asked Questions

What is TeaRAGs-MCP used for?
TeaRAGs-MCP is a devops tool. A high-performance MCP server for intelligent codebase analysis. Enterprise-ready: handles millions of LOC with fast local indexing. Semantic code search, git-aware metadata (authorship, churn, task I.
Is TeaRAGs-MCP free?
License: MIT. TeaRAGs-MCP has 10 GitHub stars.
Is TeaRAGs-MCP safe?
TeaRAGs-MCP has a Nerq Trust Score of 68/100 (C). Use with caution.
What are alternatives to TeaRAGs-MCP?
Top alternatives: ansible, Flowise, learn-claude-code. See full comparison.

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

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