What is GitLab MR Confluence Linker?
GitLab MR Confluence Linker is a AI tool that Bridges GitLab merge requests with Confluence documentation, enabling automatic retrieval, analysis, and structured documentation of code changes without leaving your conversation interface.. It has a Nerq Trust Score of 39/100 (E). 0 GitHub stars. Published by https://github.com/codebywaqas/mrconfluencelinker-mcp-server. Last analyzed September 2026.
Why This Score
- ⚠️ Security: 0/100 — Some security concerns
- ⚠️ Maintenance: 0/100 — Maintenance activity is low
- ⚠️ Community: 0 stars, 0 downloads — Growing community
- ⚠️ Transparency: License: Not specified — No license specified
Trust & Safety Overview
What GitLab MR Confluence Linker Does
GitLab MR Confluence Linker is a mcp_server in the AI tool category. Bridges GitLab merge requests with Confluence documentation, enabling automatic retrieval, analysis, and structured documentation of code changes without leaving your conversation interface.. It is published by https://github.com/codebywaqas/mrconfluencelinker-mcp-server and has no specified license. With 0 GitHub stars and 0 downloads, it has a small community of users and contributors.
Who Should Use GitLab MR Confluence Linker
GitLab MR Confluence Linker is recommended only for experimental use. Consider alternatives with higher trust scores for production systems.
Details
| Author | https://github.com/codebywaqas/mrconfluencelinker-mcp-server |
|---|---|
| Category | AI tool |
| License | Not specified |
| Type | mcp_server |
| Source | View on GitHub |
| Security Score | 0/100 |
| Activity Score | 0/100 |
How to Get Started
Check the trust score before installing:
curl nerq.ai/v1/preflight?target=gitlab-mr-confluence-linker
Setup guide · Full safety report · Production review · Is it safe?
Safer Alternatives
| Tool | Trust | Stars |
|---|---|---|
| MarkItDown | 42 | 92.8K |
| Sequential Thinking | 50 | 89.4K |
| Filesystem | 50 | 89.4K |
| Time | 50 | 89.4K |
| Fetch | 50 | 89.4K |
Frequently Asked Questions
Last updated September 2026. Trust scores based on automated analysis of public data.