Is Blender Mcp Safe?
Blender Mcp — Nerq Trust Score 58.0/100 (C grade). Score based on 2 independent trust signals. Last analyzed: 2026-03-20
Blender Mcp is a Python package with a Nerq Trust Score of 58.0/100 (C), based on 3 independent data dimensions. Last analyzed: 2026-03-20 Security: 90/100. Popularity: 0/100. Data sourced from PyPI registry, GitHub repository, NVD, OSV.dev, and OpenSSF Scorecard. Last updated: 2026-03-20. Machine-readable data (JSON).
Is Blender Mcp safe?
Trust Score Breakdown — Blender Mcp has a Nerq Trust Score of 58.0/100 (C). Measured across 2 independent trust signals (as of 2026-03-20).
What is Blender Mcp's trust score?
Blender Mcp has a Nerq Trust Score of 58.0/100, 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 Blender Mcp?
Blender Mcp's strongest signal is security at 90/100. No known vulnerabilities have been detected.
What is Blender Mcp and who maintains it?
| Author | Unknown |
| Category | Python Packages |
| Source | N/A |
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Safety Guide: Blender Mcp
What is Blender Mcp?
Blender Mcp is a Python package — Blender integration through the Model Context Protocol.
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=blender-mcp
Key Safety Concerns for Python package
When evaluating any Python package, watch for: dependency vulnerabilities, malicious uploads, maintenance status.
Measured Signals
Blender Mcp has a Nerq Trust Score of 58/100 (C). This score is a composite of automated measurements of security, maintenance, community, and quality signals.
Key Takeaways
- Blender Mcp has a Trust Score of 58/100 (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.