Is Ninjaone Mcp Safe?
Ninjaone Mcp — Nerq Trust Score 74.8/100 (B grade). Based on analysis of 5 trust dimensions, it is generally safe but has some concerns. Last updated: 2026-04-25.
Yes, Ninjaone Mcp is safe to use. Ninjaone Mcp is a software tool with a Nerq Trust Score of 74.8/100 (B), based on 5 independent data dimensions. Recommended for use. Security: 0/100. Maintenance: 1/100. Popularity: 0/100. Data sourced from multiple public sources including package registries, GitHub, NVD, OSV.dev, and OpenSSF Scorecard. Last updated: 2026-04-25. Machine-readable data (JSON).
Is Ninjaone Mcp safe?
YES — Ninjaone Mcp has a Nerq Trust Score of 74.8/100 (B). It meets Nerq's trust threshold with strong signals across security, maintenance, and community adoption. Recommended for use — review the full report below for specific considerations.
What is Ninjaone Mcp's trust score?
Ninjaone Mcp has a Nerq Trust Score of 74.8/100, earning a B grade. This score is based on 5 independently measured dimensions including security, maintenance, and community adoption.
What are the key security findings for Ninjaone Mcp?
Ninjaone Mcp's strongest signal is compliance at 100/100. No known vulnerabilities have been detected. It meets the Nerq Verified threshold of 70+.
What is Ninjaone Mcp and who maintains it?
| Author | wyre-technology |
| Category | Infrastructure |
| Stars | 1 |
| Source | https://github.com/wyre-technology/ninjaone-mcp |
| Frameworks | anthropic |
| Protocols | mcp · rest |
Regulatory Compliance
| EU AI Act Risk Class | MINIMAL |
| Compliance Score | 100/100 |
| Jurisdictions | Assessed across 52 jurisdictions |
Popular Alternatives in infrastructure
What Is Ninjaone Mcp?
Ninjaone Mcp is a software tool in the infrastructure category: NinjaOne MCP Server with a decision tree architecture for efficient tool loading.. It has 1 GitHub stars. Nerq Trust Score: 75/100 (B).
Nerq independently analyzes every software tool, app, and extension across multiple trust signals including security vulnerabilities, maintenance activity, license compliance, and community adoption.
How Nerq Assesses Ninjaone Mcp's Safety
Nerq's Trust Score is calculated from 13+ independent signals aggregated into five dimensions. Here is how Ninjaone Mcp performs in each:
- Security (0/100): Ninjaone Mcp's security posture is poor. This score factors in known CVEs, dependency vulnerabilities, security policy presence, and code signing practices.
- Maintenance (1/100): Ninjaone Mcp is potentially abandoned. We track commit frequency, release cadence, issue response times, and PR merge rates.
- Documentation (1/100): Documentation quality is insufficient. This includes README completeness, API documentation, usage examples, and contribution guidelines.
- Compliance (100/100): Ninjaone Mcp is broadly compliant. Assessed against regulations in 52 jurisdictions including the EU AI Act, CCPA, and GDPR.
- Community (0/100): Community adoption is limited. Based on GitHub stars, forks, download counts, and ecosystem integrations.
The overall Trust Score of 74.8/100 (B) reflects the weighted combination of these signals. This exceeds the Nerq Verified threshold of 70, indicating the tool meets our standards for production use.
Who Should Use Ninjaone Mcp?
Ninjaone Mcp is designed for:
- Developers and teams working with infrastructure tools
- Organizations evaluating AI tools for their stack
- Researchers exploring AI capabilities in this domain
Risk guidance: Ninjaone Mcp meets the minimum threshold for production use, but we recommend monitoring for security advisories and keeping dependencies up to date. Consider implementing additional guardrails for sensitive workloads.
How to Verify Ninjaone Mcp's Safety Yourself
While Nerq provides automated trust analysis, we recommend these additional steps before adopting any software tool:
- Check the source code — Review the repository's security policy, open issues, and recent commits for signs of active maintenance.
- Scan dependencies — Use tools like
npm audit,pip-audit, orsnykto check for known vulnerabilities in Ninjaone Mcp's dependency tree. - Review permissions — Understand what access Ninjaone Mcp requires. Software tools should follow the principle of least privilege.
- Test in isolation — Run Ninjaone Mcp in a sandboxed environment before granting access to production data or systems.
- Monitor continuously — Use Nerq's API to set up automated trust checks:
GET nerq.ai/v1/preflight?target=ninjaone-mcp - Review the license — Confirm that Ninjaone Mcp's license is compatible with your intended use case. Pay attention to restrictions on commercial use, redistribution, and derivative works. Some AI tools use dual licensing or have separate terms for enterprise customers that differ from the open-source license.
- Check community signals — Look at the project's issue tracker, discussion forums, and social media presence. A healthy community actively reports bugs, contributes fixes, and discusses security concerns openly. Low community engagement may indicate limited peer review of the codebase.
Common Safety Concerns with Ninjaone Mcp
When evaluating whether Ninjaone Mcp is safe, consider these category-specific risks:
Understand how Ninjaone Mcp processes, stores, and transmits your data. Review the tool's privacy policy and data retention practices, especially for sensitive or proprietary information.
Check Ninjaone Mcp's dependency tree for known vulnerabilities. Tools with outdated or unmaintained dependencies pose a higher security risk.
Regularly check for updates to Ninjaone Mcp. Security patches and bug fixes are only effective if you're running the latest version.
If Ninjaone Mcp connects to external APIs or services, each integration point is a potential attack surface. Audit all third-party connections, verify that data shared with external services is minimized, and ensure that integration credentials are rotated regularly.
Verify that Ninjaone Mcp's license is compatible with your intended use case. Some AI tools have restrictive licenses that limit commercial use, redistribution, or derivative works. Using Ninjaone Mcp in violation of its license can expose your organization to legal liability.
Ninjaone Mcp and the EU AI Act
Ninjaone Mcp is classified as Minimal Risk under the EU AI Act. This is the lowest risk category, meaning it faces minimal regulatory requirements. However, transparency obligations still apply.
Nerq's compliance assessment covers 52 jurisdictions worldwide. For organizations deploying AI tools in regulated environments, understanding these classifications is essential for legal compliance.
Best Practices for Using Ninjaone Mcp Safely
Whether you're an individual developer or an enterprise team, these practices will help you get the most from Ninjaone Mcp while minimizing risk:
Periodically review how Ninjaone Mcp is used in your workflow. Check for unexpected behavior, permissions drift, and compliance with your security policies.
Ensure Ninjaone Mcp and all its dependencies are running the latest stable versions to benefit from security patches.
Grant Ninjaone Mcp only the minimum permissions it needs to function. Avoid granting admin or root access.
Subscribe to Ninjaone Mcp's security advisories and vulnerability disclosures. Use Nerq's API to get automated trust score updates.
Create and maintain a clear policy for how Ninjaone Mcp is used within your organization, including data handling guidelines and acceptable use cases.
When Should You Avoid Ninjaone Mcp?
Even well-trusted tools aren't right for every situation. Consider avoiding Ninjaone Mcp in these scenarios:
- Scenarios where Ninjaone Mcp's specific capabilities exceed your actual needs — simpler tools may be safer
- Air-gapped environments where the tool cannot receive security updates
- Projects with strict regulatory requirements that haven't been explicitly validated
For each scenario, evaluate whether Ninjaone Mcp's trust score of 74.8/100 meets your organization's risk tolerance. The Nerq Verified status indicates general production readiness, but sector-specific requirements may apply.
How Ninjaone Mcp Compares to Industry Standards
Nerq indexes over 6 million software tools, apps, and packages across dozens of categories. Among infrastructure tools, the average Trust Score is 62/100. Ninjaone Mcp's score of 74.8/100 is significantly above the category average of 62/100.
This places Ninjaone Mcp in the top tier of infrastructure tools that Nerq tracks. Tools scoring this far above average typically demonstrate mature security practices, consistent release cadence, and broad community adoption.
Industry benchmarks matter because they contextualize a tool's safety profile. A score that looks moderate in isolation may actually represent strong performance within a challenging category — or vice versa. Nerq's category-relative analysis helps teams make informed decisions by showing not just absolute quality, but how a tool ranks against its direct peers.
Trust Score History
Nerq continuously monitors Ninjaone Mcp and recalculates its Trust Score as new data becomes available. Our scoring engine ingests real-time signals from source repositories, vulnerability databases (NVD, OSV.dev), package registries, and community metrics. When a new CVE is published, a major release ships, or maintenance patterns change, Ninjaone Mcp's score is updated within 24 hours.
Historical trust trends reveal whether a tool is improving, stable, or declining over time. A tool that consistently maintains or improves its score demonstrates ongoing commitment to security and quality. Conversely, a downward trend may signal reduced maintenance, growing technical debt, or unresolved vulnerabilities. To track Ninjaone Mcp's score over time, use the Nerq API: GET nerq.ai/v1/preflight?target=ninjaone-mcp&include=history
Nerq retains trust score snapshots at regular intervals, enabling trend analysis across weeks and months. Enterprise users can access detailed historical reports showing how each dimension — security, maintenance, documentation, compliance, and community — has evolved independently, providing granular visibility into which aspects of Ninjaone Mcp are strengthening or weakening over time.
Ninjaone Mcp vs Alternatives
In the infrastructure category, Ninjaone Mcp scores 74.8/100. There are higher-scoring alternatives available. For a detailed comparison, see:
- Ninjaone Mcp vs n8n — Trust Score: 52.2/100
- Ninjaone Mcp vs langflow — Trust Score: 66.1/100
- Ninjaone Mcp vs dify — Trust Score: 65.5/100
Key Takeaways
- Ninjaone Mcp has a Trust Score of 74.8/100 (B) and is Nerq Verified.
- Ninjaone Mcp meets the minimum threshold for production deployment, though monitoring and additional guardrails are recommended.
- Among infrastructure tools, Ninjaone Mcp scores significantly above the category average of 62/100, demonstrating above-average reliability.
- Always verify safety independently — use Nerq's Preflight API for automated, up-to-date trust checks before integration.
Detailed Score Analysis
| Dimension | Score |
|---|---|
| Security | 0/100 |
| Maintenance | 1/100 |
| Popularity | 0/100 |
Based on 3 dimensions. Data from multiple public sources including package registries, GitHub, NVD, OSV.dev, and OpenSSF Scorecard.
What data does Ninjaone Mcp collect?
Privacy assessment for Ninjaone Mcp is not yet available. See our methodology for how Nerq measures privacy, or the public privacy review for any community-contributed notes.
Is Ninjaone Mcp secure?
Security score: 0/100. Review security practices and consider alternatives with higher security scores for sensitive use cases.
Nerq monitors this entity against NVD, OSV.dev, and registry-specific vulnerability databases for ongoing security assessment.
Full analysis: Ninjaone Mcp Security Report
How we calculated this score
Ninjaone Mcp's trust score of 74.8/100 (B) is computed from multiple public sources including package registries, GitHub, NVD, OSV.dev, and OpenSSF Scorecard. The score reflects 3 independent dimensions: security (0/100), maintenance (1/100), popularity (0/100). Each dimension is weighted equally to produce the composite trust score.
Nerq analyzes over 7.5 million entities across 26 registries using the same methodology, enabling direct cross-entity comparison. Scores are updated continuously as new data becomes available.
This page was last reviewed on April 25, 2026. Data version: 1.0.
Full methodology documentation · Machine-readable data (JSON API)
Frequently Asked Questions
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See Also
Disclaimer: Nerq trust scores are automated assessments based on publicly available signals. They are not endorsements or guarantees. Always conduct your own due diligence.