Mcp Abstract é seguro?
Mcp Abstract — Nerq Trust Score 62.0/100 (Grau C). Pontuação baseada em 5 independent trust signals.
Mcp Abstract é um software tool com um Nerq Trust Score de 62.0/100 (C), com base em 5 dimensões de dados independentes. Segurança: 0/100. Manutenção: 1/100. Popularidade: 0/100. Dados obtidos de múltiplas fontes públicas incluindo registros de pacotes, GitHub, NVD, OSV.dev e OpenSSF Scorecard. Última atualização: n/a. Dados legíveis por máquina (JSON).
Mcp Abstract é seguro?
Detalhamento da Pontuação de Confiança — Mcp Abstract has a Nerq Trust Score of 62.0/100 (C). Measured across 5 independent trust signals.
Qual é a pontuação de confiança de Mcp Abstract?
Mcp Abstract tem uma Pontuação de Confiança Nerq de 62.0/100, obtendo grau C. Esta pontuação é baseada em 5 dimensões medidas independentemente.
Quais são as principais descobertas de segurança de Mcp Abstract?
O sinal mais forte de Mcp Abstract é conformidade com 100/100. Nenhuma vulnerabilidade conhecida foi detectada.
O que é Mcp Abstract e quem o mantém?
| Autor | NimbleBrainInc |
| Categoria | Data |
| Source | https://github.com/NimbleBrainInc/mcp-abstract |
| Frameworks | anthropic |
| Protocols | mcp · rest |
Conformidade Regulatória
| EU AI Act Risk Class | MINIMAL |
| Compliance Score | 100/100 |
| Jurisdictions | Assessed across 52 jurisdictions |
Alternativas Populares em data
Mcp Abstract em outras plataformas
Mesmo desenvolvedor/empresa em outros registros:
What Is Mcp Abstract?
Mcp Abstract is a software tool in the data category: MCP server for Abstract API - validate emails, phone numbers, enrich company data, IP geolocation, and currency conversion.. Nerq Trust Score: 62/100 (C).
Nerq independently analyzes every software tool, app, and extension across multiple trust signals including segurança vulnerabilities, manutenção activity, license conformidade, and adoção pela comunidade.
How Nerq Assesses Mcp Abstract's Safety
Nerq's Trust Score is calculated from 13+ independent signals aggregated into five dimensões. Here is how Mcp Abstract performs in each:
- Segurança (0/100): Mcp Abstract's segurança posture is poor. This score factors in known CVEs, dependency vulnerabilities, segurança policy presence, and code signing practices.
- Manutenção (1/100): Mcp Abstract 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 documentação, usage examples, and contribution guidelines.
- Compliance (100/100): Mcp Abstract is broadly compliant. Assessed against regulations in 52 jurisdictions including the EU AI Act, CCPA, and GDPR.
- Community (0/100): Community adoption is limited. Baseado em GitHub stars, forks, download counts, and ecosystem integrations.
The overall Trust Score of 62.0/100 (C) 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 Mcp Abstract?
Mcp Abstract is commonly evaluated by:
- Developers and teams working with data tools
- Organizations evaluating AI tools for their stack
- Researchers exploring AI capabilities in this domain
How to read the signals: Mcp Abstract's measured signals (segurança 0/100, manutenção 1/100, documentação 1/100, 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 Mcp Abstract's Safety Yourself
While Nerq provides automated trust analysis, we recommend these additional steps before adopting any software tool:
- Check the source code — Revise o/a repository's segurança policy, open issues, and recent commits for signs of active manutenção.
- Scan dependencies — Use tools like
npm audit,pip-audit, orsnykto check for known vulnerabilities in Mcp Abstract's dependency tree. - Avaliação permissions — Understand what access Mcp Abstract requires. Software tools should follow the principle of least privilege.
- Test in isolation — Run Mcp Abstract 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=mcp-abstract - Revise o/a license — Confirm that Mcp Abstract'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 segurança concerns openly. Low community engagement may indicate limited peer review of the codebase.
Common Safety Concerns with Mcp Abstract
When evaluating whether Mcp Abstract is safe, consider these category-specific risks:
Understand how Mcp Abstract processes, stores, and transmits your data. Revise o/a tool's privacy policy and data retention practices, especially for sensitive or proprietary information.
Check Mcp Abstract's dependency tree for known vulnerabilities. Tools with outdated or unmaintained dependencies pose a higher segurança risk.
Regularly check for updates to Mcp Abstract. Segurança patches and bug fixes are only effective if you're running the latest version.
If Mcp Abstract 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 Mcp Abstract's license is compatible with your intended use case. Some AI tools have restrictive licenses that limit commercial use, redistribution, or derivative works. Using Mcp Abstract in violation of its license can expose your organization to legal liability.
Mcp Abstract and the EU AI Act
Mcp Abstract 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 conformidade assessment covers 52 jurisdictions worldwide. For organizations deploying AI tools in regulated environments, understanding these classifications is essential for legal conformidade.
Best Practices for Using Mcp Abstract Safely
Whether you're an individual developer or an enterprise team, these practices will help you get the most from Mcp Abstract while minimizing risk:
Periodically review how Mcp Abstract is used in your workflow. Check for unexpected behavior, permissions drift, and conformidade with your segurança policies.
Ensure Mcp Abstract and all its dependencies are running the latest stable versions to benefit from segurança patches.
Grant Mcp Abstract only the minimum permissions it needs to function. Avoid granting admin or root access.
Subscribe to Mcp Abstract's segurança advisories and vulnerability disclosures. Use Nerq's API to get automated trust score updates.
Create and maintain a clear policy for how Mcp Abstract is used within your organization, including data handling guidelines and acceptable use cases.
Situations That Warrant Independent Review of Mcp Abstract
Nerq's signals are one input. In the following situations, evaluate Mcp Abstract's measured signals against your own requirements before making a decision:
- Environments handling sensitive or regulated data (healthcare, finance, government)
- Mission-critical systems where downtime has significant business impact
- Deployments with strict regulatory requirements that must be independently validated
For each situation, compare Mcp Abstract's measured trust score of 62.0/100 and its individual signals against your organization's own criteria. Nerq does not assert whether Mcp Abstract is suitable for any particular use.
How Mcp Abstract Compares to Industry Standards
Nerq indexes over 6 million software tools, apps, and packages across dozens of categories. Among data tools, the average Trust Score is 62/100. Mcp Abstract's score of 62.0/100 is near the category average of 62/100.
This places Mcp Abstract in line with the typical data tool tool. It meets baseline expectations but does not distinguish itself from peers on trust metrics.
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 Mcp Abstract 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 manutenção patterns change, Mcp Abstract'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 segurança and quality. Conversely, a downward trend may signal reduced manutenção, growing technical debt, or unresolved vulnerabilities. To track Mcp Abstract's score over time, use the Nerq API: GET nerq.ai/v1/preflight?target=mcp-abstract&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 — segurança, manutenção, documentação, conformidade, and community — has evolved independently, providing granular visibility into which aspects of Mcp Abstract are strengthening or weakening over time.
Mcp Abstract vs Alternativas
In the data category, Mcp Abstract scores 62.0/100. There are higher-scoring alternatives available. For a detailed comparison, see:
- Mcp Abstract vs firecrawl — Trust Score: 64.4/100
- Mcp Abstract vs MinerU — Trust Score: 76.6/100
- Mcp Abstract vs mindsdb — Trust Score: 68.1/100
Pontos Principais
- Mcp Abstract has a measured Nerq Trust Score of 62.0/100 (C) — a composite of independent signals, not a suitability judgment.
- Among data tools, Mcp Abstract scores near the category average of 62/100 (a positional measurement relative to peers).
- The individual signals — segurança, manutenção, documentação, conformidade, community — are shown above. Weigh them against your own requirements.
- Query the current measured values via Nerq's Preflight API.
Perguntas Frequentes
Mcp Abstract é seguro?
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Quais são alternativas mais seguras ao Mcp Abstract?
Com que frequência o score de segurança do Mcp Abstract é atualizado?
Posso usar Mcp Abstract em um ambiente regulado?
Veja também
Disclaimer: As pontuações de confiança da Nerq são avaliações automatizadas baseadas em sinais publicamente disponíveis. Não são endossos ou garantias. Sempre realize sua própria verificação.