¿Es Human Mcp Seguro?

Human Mcp — Nerq Trust Score 45.6/100 (Grado D). Puntuación basada en 3 independent trust signals.

Human Mcp es un software tool con un Nerq Trust Score de 45.6/100 (D), basado en 3 dimensiones de datos independientes. Mantenimiento: 0/100. Popularidad: 0/100. Datos de múltiples fuentes públicas incluyendo registros de paquetes, GitHub, NVD, OSV.dev y OpenSSF Scorecard. Última actualización: n/a. Datos legibles por máquina (JSON).

¿Es Human Mcp Seguro?

Desglose de Puntuación de Confianza — Human Mcp has a Nerq Trust Score of 45.6/100 (D). Measured across 3 independent trust signals.

Análisis de Seguridad → Informe de Privacidad de Human Mcp →

¿Cuál es la puntuación de confianza de Human Mcp?

Human Mcp tiene una Puntuación de Confianza Nerq de 45.6/100, obteniendo un grado D. Esta puntuación se basa en 3 dimensiones medidas independientemente.

Mantenimiento
0
Documentación
0
Popularidad
0

¿Cuáles son los hallazgos de seguridad clave de Human Mcp?

La señal más fuerte de Human Mcp es mantenimiento con 0/100. No se han detectado vulnerabilidades conocidas.

⚠Mantenimiento: 0/100 — baja actividad de mantenimiento
⚠Documentación: 0/100 — documentación limitada
⚠Popularidad: 0/100 — 45 estrellas en pulsemcp

¿Qué es Human Mcp y quién lo mantiene?

Autorhttps://github.com/upamune/human-mcp
CategoríaInfrastructure
Estrellas45
Fuentehttps://github.com/upamune/human-mcp

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What Is Human Mcp?

Human Mcp is a software tool in the infrastructure category: Human-in-the-loop server that routes AI assistant requests to a human operator through a Streamlit UI, enabling creative and unpredictable responses while maintaining the illusion of automated interactions.. It has 45 GitHub stars. Nerq Trust Score: 46/100 (D).

Nerq independently analyzes every software tool, app, and extension across multiple trust signals including seguridad vulnerabilities, mantenimiento activity, license cumplimiento, and adopción por la comunidad.

How Nerq Assesses Human Mcp's Safety

Nerq's Trust Score is calculated from 13+ independent signals aggregated into five dimensiones. Here is how Human Mcp performs in each:

The overall Trust Score of 45.6/100 (D) is the weighted combination of these measured signals. It is a measurement, not a pass/fail or suitability judgment — weigh the individual signals against your own requirements.

Who Typically Evaluates Human Mcp?

Human Mcp is commonly evaluated by:

How to read the signals: Human Mcp's measured signals (mantenimiento 0/100, documentación 0/100, community 0/100) are shown above. These are measurements, not a suitability judgment — weigh each signal against the requirements of your own use case and risk tolerance.

How to Verify Human Mcp's Safety Yourself

While Nerq provides automated trust analysis, we recommend these additional steps before adopting any software tool:

  1. Check the source code — Revisar el/la repository seguridad policy, open issues, and recent commits for signs of active mantenimiento.
  2. Scan dependencies — Use tools like npm audit, pip-audit, or snyk to check for known vulnerabilities in Human Mcp's dependency tree.
  3. Reseña permissions — Understand what access Human Mcp requires. Software tools should follow the principle of least privilege.
  4. Test in isolation — Run Human Mcp in a sandboxed environment before granting access to production data or systems.
  5. Monitor continuously — Use Nerq's API to set up automated trust checks: GET nerq.ai/v1/preflight?target=Human MCP
  6. Revisar el/la license — Confirm that Human 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.
  7. 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 seguridad concerns openly. Low community engagement may indicate limited peer review of the codebase.

Common Safety Concerns with Human Mcp

When evaluating whether Human Mcp is safe, consider these category-specific risks:

Data handling

Understand how Human Mcp processes, stores, and transmits your data. Revisar el/la tool's privacy policy and data retention practices, especially for sensitive or proprietary information.

Dependency seguridad

Check Human Mcp's dependency tree for known vulnerabilities. Tools with outdated or unmaintained dependencies pose a higher seguridad risk.

Update frequency

Regularly check for updates to Human Mcp. Seguridad patches and bug fixes are only effective if you're running the latest version.

Third-party integrations

If Human 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.

License and IP cumplimiento

Verify that Human 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 Human Mcp in violation of its license can expose your organization to legal liability.

Best Practices for Using Human Mcp Safely

Whether you're an individual developer or an enterprise team, these practices will help you get the most from Human Mcp while minimizing risk:

Conduct regular audits

Periodically review how Human Mcp is used in your workflow. Check for unexpected behavior, permissions drift, and cumplimiento with your seguridad policies.

Keep dependencies updated

Ensure Human Mcp and all its dependencies are running the latest stable versions to benefit from seguridad patches.

Follow least privilege

Grant Human Mcp only the minimum permissions it needs to function. Avoid granting admin or root access.

Monitor for seguridad advisories

Subscribe to Human Mcp's seguridad advisories and vulnerability disclosures. Use Nerq's API to get automated trust score updates.

Document usage policies

Create and maintain a clear policy for how Human Mcp is used within your organization, including data handling guidelines and acceptable use cases.

Situations That Warrant Independent Review of Human Mcp

Nerq's signals are one input. In the following situations, evaluate Human Mcp's measured signals against your own requirements before making a decision:

For each situation, compare Human Mcp's measured trust score of 45.6/100 and its individual signals against your organization's own criteria. Nerq does not assert whether Human Mcp is suitable for any particular use.

How Human 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. Human Mcp's score of 45.6/100 is below the category average of 62/100.

This suggests that Human Mcp trails behind many comparable infrastructure tools. Organizations with strict seguridad requirements should evaluate whether higher-scoring alternatives better meet their needs.

Industry benchmarks matter because they contextualize a tool's safety profile. A score that looks moderado 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 Human 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 mantenimiento patterns change, Human 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 seguridad and quality. Conversely, a downward trend may signal reduced mantenimiento, growing technical debt, or unresolved vulnerabilities. To track Human Mcp's score over time, use the Nerq API: GET nerq.ai/v1/preflight?target=Human 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 — seguridad, mantenimiento, documentación, cumplimiento, and community — has evolved independently, providing granular visibility into which aspects of Human Mcp are strengthening or weakening over time.

Human Mcp vs Alternativas

In the infrastructure category, Human Mcp scores 45.6/100. There are higher-scoring alternatives available. For a detailed comparison, see:

Puntos Clave

Preguntas Frecuentes

¿Es Human Mcp Seguro?
Human MCP con un Nerq Trust Score de 45.6/100 (D). Señal más fuerte: mantenimiento (0/100). Puntuación basada en Mantenimiento (0/100), Popularidad (0/100), Documentación (0/100).
¿Cuál es la puntuación de confianza de Human Mcp?
Human MCP: 45.6/100 (D). Puntuación basada en Mantenimiento (0/100), Popularidad (0/100), Documentación (0/100). Las puntuaciones se actualizan cuando hay nuevos datos. API: GET nerq.ai/v1/preflight?target=Human MCP
¿Cuáles son alternativas más seguras a Human Mcp?
En la categoría Infrastructure, higher-rated alternatives include n8n-io/n8n (69/100), langflow-ai/langflow (77/100), langgenius/dify (70/100). Human MCP scores 45.6/100.
¿Con qué frecuencia se actualiza la puntuación de Human Mcp?
Nerq recomputes Human Mcp's trust score as new data becomes available. Current: 45.6/100 (D). API: GET nerq.ai/v1/preflight?target=Human MCP
¿Puedo usar Human Mcp en un entorno regulado?
Human Mcp: 45.6/100 (D). Compliance signals are shown in the breakdown above. Evaluate against your own regulatory requirements.
API: /v1/preflight Trust Badge API Docs

Ver también

Disclaimer: Las puntuaciones de confianza de Nerq son evaluaciones automatizadas basadas en señales disponibles públicamente. No son respaldos ni garantías. Siempre realice su propia diligencia debida.

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