Is Atlas Mcp Safe?
Atlas Mcp — Nerq Trust Score 50.8/100 (C- grade). Score based on 2 independent trust signals. Last analyzed: 2026-03-20
Atlas Mcp is a Python package with a Nerq Trust Score of 50.8/100 (C-), based on 3 independent data dimensions. Last analyzed: 2026-03-20 Security: 90/100. Popularity: 15/100. Data sourced from PyPI registry, GitHub repository, NVD, OSV.dev, and OpenSSF Scorecard. Last updated: 2026-03-20. Machine-readable data (JSON).
Is Atlas Mcp safe?
Trust Score Breakdown — Atlas Mcp has a Nerq Trust Score of 50.8/100 (C-). Measured across 2 independent trust signals (as of 2026-03-20).
What is Atlas Mcp's trust score?
Atlas Mcp has a Nerq Trust Score of 50.8/100, earning a C- grade. This score is based on 2 independently measured dimensions including security, maintenance, and community adoption.
What are the key security findings for Atlas Mcp?
Atlas Mcp's strongest signal is security at 90/100. No known vulnerabilities have been detected.
What is Atlas Mcp and who maintains it?
| Author | rsanandres |
| Category | Python Packages |
| Source | N/A |
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Safety Guide: Atlas Mcp
What is Atlas Mcp?
Atlas Mcp is a Python package — AI-powered MCP server for FHIR document search and conversation.
How to Verify Safety
Run pip audit or safety check. Review on PyPI for download stats.
You can also check the trust score via API: GET /v1/preflight?target=atlas-mcp
Key Safety Concerns for Python package
When evaluating any Python package, watch for: dependency vulnerabilities, malicious uploads, maintenance status.
Measured Signals
Atlas Mcp has a Nerq Trust Score of 51/100 (C-). This score is a composite of automated measurements of security, maintenance, community, and quality signals.
Key Takeaways
- Atlas Mcp has a Trust Score of 51/100 (C-).
- The score is a measured composite — it is not a suitability judgment. Evaluate the individual signals against your own requirements.
- Query the current measured values via the Nerq API.
Frequently Asked Questions
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Disclaimer: Nerq trust scores are automated measurements based on publicly available signals. They are not endorsements, verdicts, or guarantees of suitability. Always evaluate the signals against your own requirements.