Apakah Dollhousemcp Aman?

Dollhousemcp — Nerq Trust Score 45.6/100 (Nilai D). Skor berdasarkan 3 independent trust signals.

Dollhousemcp adalah software tool dengan Skor Kepercayaan Nerq sebesar 45.6/100 (D), based on 3 dimensi data independen. Pemeliharaan: 0/100. Popularitas: 0/100. Data bersumber dari berbagai sumber publik termasuk registri paket, GitHub, NVD, OSV.dev, dan OpenSSF Scorecard. Terakhir diperbarui: n/a. Data yang dapat dibaca mesin (JSON).

Apakah Dollhousemcp Aman?

Rincian Skor Kepercayaan — Dollhousemcp has a Nerq Trust Score of 45.6/100 (D). Measured across 3 independent trust signals.

Analisis Keamanan → Laporan Privasi Dollhousemcp →

Berapa skor kepercayaan Dollhousemcp?

Dollhousemcp memiliki Skor Kepercayaan Nerq 45.6/100 dengan nilai D. Skor ini didasarkan pada 3 dimensi yang diukur secara independen.

Pemeliharaan
0
Dokumentasi
0
Popularitas
0

Apa temuan keamanan utama untuk Dollhousemcp?

Sinyal terkuat Dollhousemcp adalah pemeliharaan pada 0/100. Tidak ada kerentanan yang diketahui terdeteksi.

⚠Pemeliharaan: 0/100 — aktivitas pemeliharaan rendah
⚠Dokumentasi: 0/100 — dokumentasi terbatas
⚠Popularitas: 0/100 — 28 bintang di pulsemcp

Apa itu Dollhousemcp dan siapa yang mengelolanya?

Pembuathttps://github.com/dollhousemcp/mcp-server
KategoriInfrastructure
Bintang28
Sumberhttps://github.com/dollhousemcp/mcp-server

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What Is Dollhousemcp?

Dollhousemcp is a software tool in the infrastructure category: Enables dynamic persona management from markdown files with local creation, GitHub marketplace integration, and behavioral switching capabilities for role-playing and domain-specific customization.. It has 28 GitHub stars. Nerq Trust Score: 46/100 (D).

Nerq independently analyzes every software tool, app, and extension across multiple trust signals including keamanan vulnerabilities, pemeliharaan activity, license kepatuhan, and adopsi komunitas.

How Nerq Assesses Dollhousemcp's Safety

Nerq's Trust Score is calculated from 13+ independent signals aggregated into five dimensi. Here is how Dollhousemcp 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 Dollhousemcp?

Dollhousemcp is commonly evaluated by:

How to read the signals: Dollhousemcp's measured signals (pemeliharaan 0/100, dokumentasi 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 Dollhousemcp's Safety Yourself

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

  1. Check the source code — Tinjau repository keamanan policy, open issues, and recent commits for signs of active pemeliharaan.
  2. Scan dependencies — Use tools like npm audit, pip-audit, or snyk to check for known vulnerabilities in Dollhousemcp's dependency tree.
  3. Ulasan permissions — Understand what access Dollhousemcp requires. Software tools should follow the principle of least privilege.
  4. Test in isolation — Run Dollhousemcp 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=DollhouseMCP
  6. Tinjau license — Confirm that Dollhousemcp'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 keamanan concerns openly. Low community engagement may indicate limited peer review of the codebase.

Common Safety Concerns with Dollhousemcp

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

Data handling

Understand how Dollhousemcp processes, stores, and transmits your data. Tinjau tool's privacy policy and data retention practices, especially for sensitive or proprietary information.

Dependency keamanan

Check Dollhousemcp's dependency tree for known vulnerabilities. Tools with outdated or unmaintained dependencies pose a higher keamanan risk.

Update frequency

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

Third-party integrations

If Dollhousemcp 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 kepatuhan

Verify that Dollhousemcp's license is compatible with your intended use case. Some AI tools have restrictive licenses that limit commercial use, redistribution, or derivative works. Using Dollhousemcp in violation of its license can expose your organization to legal liability.

Best Practices for Using Dollhousemcp Safely

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

Conduct regular audits

Periodically review how Dollhousemcp is used in your workflow. Check for unexpected behavior, permissions drift, and kepatuhan with your keamanan policies.

Keep dependencies updated

Ensure Dollhousemcp and all its dependencies are running the latest stable versions to benefit from keamanan patches.

Follow least privilege

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

Monitor for keamanan advisories

Subscribe to Dollhousemcp's keamanan 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 Dollhousemcp is used within your organization, including data handling guidelines and acceptable use cases.

Situations That Warrant Independent Review of Dollhousemcp

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

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

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

This suggests that Dollhousemcp trails behind many comparable infrastructure tools. Organizations with strict keamanan 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 sedang 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 Dollhousemcp 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 pemeliharaan patterns change, Dollhousemcp'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 keamanan and quality. Conversely, a downward trend may signal reduced pemeliharaan, growing technical debt, or unresolved vulnerabilities. To track Dollhousemcp's score over time, use the Nerq API: GET nerq.ai/v1/preflight?target=DollhouseMCP&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 — keamanan, pemeliharaan, dokumentasi, kepatuhan, and community — has evolved independently, providing granular visibility into which aspects of Dollhousemcp are strengthening or weakening over time.

Dollhousemcp vs Alternatif

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

Kesimpulan Utama

Pertanyaan yang Sering Diajukan

Apakah Dollhousemcp Aman?
DollhouseMCP dengan Skor Kepercayaan Nerq sebesar 45.6/100 (D). Sinyal terkuat: pemeliharaan (0/100). Skor berdasarkan Pemeliharaan (0/100), Popularitas (0/100), Dokumentasi (0/100).
Berapa skor kepercayaan Dollhousemcp?
DollhouseMCP: 45.6/100 (D). Skor berdasarkan Pemeliharaan (0/100), Popularitas (0/100), Dokumentasi (0/100). Skor diperbarui saat data baru tersedia. API: GET nerq.ai/v1/preflight?target=DollhouseMCP
Apa alternatif yang lebih aman dari Dollhousemcp?
Dalam kategori Infrastructure, higher-rated alternatives include n8n-io/n8n (69/100), langflow-ai/langflow (77/100), langgenius/dify (70/100). DollhouseMCP scores 45.6/100.
Seberapa sering skor keamanan Dollhousemcp diperbarui?
Nerq recomputes Dollhousemcp's trust score as new data becomes available. Current: 45.6/100 (D). API: GET nerq.ai/v1/preflight?target=DollhouseMCP
Bisakah saya menggunakan Dollhousemcp di lingkungan yang diatur?
Dollhousemcp: 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

Lihat juga

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