Is Mcp Server Linkedin Safe?
Mcp Server Linkedin — Nerq Trust Score 60.0/100 (C+ grade). Score based on 2 independent trust signals. Last analyzed: 2026-11-01
Mcp Server Linkedin is a Python package with a Nerq Trust Score of 60.0/100 (C+), based on 3 independent data dimensions. Last analyzed: 2026-11-01 Security: 90/100. Popularity: 45/100. Data sourced from PyPI registry, GitHub repository, NVD, OSV.dev, and OpenSSF Scorecard. Last updated: 2026-11-01. Machine-readable data (JSON).
Is Mcp Server Linkedin safe?
Trust Score Breakdown — Mcp Server Linkedin has a Nerq Trust Score of 60.0/100 (C+). Measured across 2 independent trust signals (as of 2026-11-01).
What is Mcp Server Linkedin's trust score?
Mcp Server Linkedin has a Nerq Trust Score of 60.0/100, 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 Mcp Server Linkedin?
Mcp Server Linkedin's strongest signal is security at 90/100. No known vulnerabilities have been detected.
What is Mcp Server Linkedin and who maintains it?
| Author | Unknown |
| Category | Python Packages |
| Source | N/A |
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Safety Guide: Mcp Server Linkedin
What is Mcp Server Linkedin?
Mcp Server Linkedin is a Python package — MCP server that gives AI assistants like Claude access to LinkedIn profiles, companies, and job postings through the user's own browser session..
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=mcp-server-linkedin
Key Safety Concerns for Python package
When evaluating any Python package, watch for: dependency vulnerabilities, malicious uploads, maintenance status.
Measured Signals
Mcp Server Linkedin has a Nerq Trust Score of 60/100 (C+). This score is a composite of automated measurements of security, maintenance, community, and quality signals.
Key Takeaways
- Mcp Server Linkedin has a Trust Score of 60/100 (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.