Is Mcpjungle Safe?

Mcpjungle — Nerq Trust Score 52.4/100 (D grade). Score based on 3 independent trust signals.

Mcpjungle is a software tool with a Nerq Trust Score of 52.4/100 (D), based on 3 independent data dimensions. Maintenance: 0/100. Popularity: 1/100. Data sourced from multiple public sources including package registries, GitHub, NVD, OSV.dev, and OpenSSF Scorecard. Last updated: n/a. Machine-readable data (JSON).

Is Mcpjungle safe?

Trust Score Breakdown — Mcpjungle has a Nerq Trust Score of 52.4/100 (D). Measured across 3 independent trust signals.

Security Analysis → Mcpjungle Privacy Report →

What is Mcpjungle's trust score?

Mcpjungle has a Nerq Trust Score of 52.4/100, earning a D grade. This score is based on 3 independently measured dimensions including security, maintenance, and community adoption.

Maintenance
0
Documentation
0
Popularity
1

What are the key security findings for Mcpjungle?

Mcpjungle's strongest signal is popularity at 1/100. No known vulnerabilities have been detected.

Maintenance: 0/100 — low maintenance activity
Documentation: 0/100 — limited documentation
Popularity: 1/100 — 893 stars on pulsemcp

What is Mcpjungle and who maintains it?

Authorhttps://github.com/mcpjungle/mcpjungle
CategoryInfrastructure
Stars893
Sourcehttps://github.com/mcpjungle/mcpjungle

Popular Alternatives in infrastructure

n8n-io/n8n
69.1/100 · C
github
langflow-ai/langflow
81.0/100 · A
github
langgenius/dify
69.7/100 · C
github
open-webui/open-webui
60.4/100 · C
github
google-gemini/gemini-cli
60.2/100 · C
github

What Is Mcpjungle?

Mcpjungle is a software tool in the infrastructure category: Self-hosted MCP tool registry and proxy server that enables centralized discovery, management, and secure access to multiple MCP servers through a unified gateway with authentication, access control, and audit trails.. It has 893 GitHub stars. Nerq Trust Score: 52/100 (D).

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 Mcpjungle's Safety

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

The overall Trust Score of 52.4/100 (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 Mcpjungle?

Mcpjungle is commonly evaluated by:

How to read the signals: Mcpjungle's measured signals (maintenance 0/100, documentation 0/100, community 1/100) 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 Mcpjungle's Safety Yourself

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

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

Common Safety Concerns with Mcpjungle

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

Data handling

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

Dependency security

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

Update frequency

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

Third-party integrations

If Mcpjungle 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 compliance

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

Best Practices for Using Mcpjungle Safely

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

Conduct regular audits

Periodically review how Mcpjungle is used in your workflow. Check for unexpected behavior, permissions drift, and compliance with your security policies.

Keep dependencies updated

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

Follow least privilege

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

Monitor for security advisories

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

Situations That Warrant Independent Review of Mcpjungle

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

For each situation, compare Mcpjungle's measured trust score of 52.4/100 and its individual signals against your organization's own criteria. Nerq does not assert whether Mcpjungle is suitable for any particular use.

How Mcpjungle 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. Mcpjungle's score of 52.4/100 is near the category average of 62/100.

This places Mcpjungle in line with the typical infrastructure 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 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 Mcpjungle 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, Mcpjungle'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 Mcpjungle's score over time, use the Nerq API: GET nerq.ai/v1/preflight?target=MCPJungle&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 Mcpjungle are strengthening or weakening over time.

Mcpjungle vs Alternatives

In the infrastructure category, Mcpjungle scores 52.4/100. There are higher-scoring alternatives available. For a detailed comparison, see:

Key Takeaways

Frequently Asked Questions

Is Mcpjungle Safe?
MCPJungle with a Nerq Trust Score of 52.4/100 (D). Strongest signal: popularity (1/100). Score based on Maintenance (0/100), Popularity (1/100), Documentation (0/100).
What is Mcpjungle's trust score?
MCPJungle: 52.4/100 (D). Score based on Maintenance (0/100), Popularity (1/100), Documentation (0/100). Scores update as new data becomes available. API: GET nerq.ai/v1/preflight?target=MCPJungle
What are safer alternatives to Mcpjungle?
In the Infrastructure category, higher-rated alternatives include n8n-io/n8n (69/100), langflow-ai/langflow (81/100), langgenius/dify (70/100). MCPJungle scores 52.4/100.
How often is Mcpjungle's safety score updated?
Nerq recomputes Mcpjungle's trust score as new data becomes available. Current: 52.4/100 (D). API: GET nerq.ai/v1/preflight?target=MCPJungle
Can I use Mcpjungle in a regulated environment?
Mcpjungle: 52.4/100 (D). Compliance signals are shown in the breakdown above. Evaluate against your own regulatory requirements.
API: /v1/preflight Trust Badge API Docs

See Also

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.

We use cookies for analytics and caching. Privacy