armor-mcp

v0.9.1 safe
3.0
Low Risk

AnomalyArmor MCP Server - Data observability tools for AI assistants

🤖 AI Analysis

Final verdict: SAFE

The package shows minimal risk indicators with no network calls, shell executions, obfuscations, or credential risks detected. The metadata risk slightly elevates concern due to incomplete author information and potential inactivity of the account.

  • No network calls detected
  • Incomplete author information
Per-check LLM notes
  • Network: No network calls detected, which is normal unless the package's functionality requires external API interactions.
  • Shell: No shell execution patterns detected, indicating no immediate risk of command injection or local privilege escalation.
  • Obfuscation: No obfuscation patterns detected, indicating low risk.
  • Credentials: No credential harvesting patterns detected, indicating low risk.
  • Metadata: The author information is incomplete and the account seems new or inactive, raising some concerns but not enough to conclusively determine malice.

📦 Package Quality Overall: Medium (6.2/10)

✦ High Test Suite 9.0

Test suite present — 9 test file(s) found

  • Test runner config found: pyproject.toml
  • 9 test file(s) detected (e.g. __init__.py)
◈ Medium Documentation 7.0

Some documentation present

  • Documentation URL: "Documentation" -> https://docs.anomalyarmor.ai/agents
  • Detailed PyPI description (7937 chars)
○ Low Contributing Guide 4.0

No contributing guide or governance files found

  • Development Status classifier >= Beta
◈ Medium Type Annotations 5.0

Partial type annotation coverage

  • 117 type-annotated function signatures detected in source
◈ Medium Multiple Contributors 6.0

Limited contributor diversity

  • 2 unique contributor(s) across 60 commits in anomalyarmor/agents
  • Two distinct contributors found

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

✓ Code Obfuscation

No obfuscation patterns detected

✓ Shell / Subprocess Execution

No shell execution patterns detected

✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

Email domain looks legitimate: anomalyarmor.ai>

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository anomalyarmor/agents appears legitimate

⚠ Maintainer History score 4.0

2 maintainer concern(s) found

  • Author name is missing or very short
  • Author "" appears to have only 1 package on PyPI (new or inactive account)
✓ Known CVE Vulnerabilities

No known vulnerabilities found in OSV database.

💡 AI App Starter Prompt

Use this prompt to build a project with armor-mcp 
Create a real-time anomaly detection dashboard using the 'armor-mcp' package in Python. This dashboard will monitor and visualize data streams from various sources (such as IoT devices, web traffic logs, etc.) in real-time, identifying any anomalies that deviate significantly from normal behavior patterns. Your task is to design and implement a fully functional mini-application that includes the following features:

1. **Data Ingestion**: Implement a feature that allows the application to ingest live data from multiple sources. These sources could include CSV files, real-time APIs, or even simulated data.
2. **Real-Time Visualization**: Develop a user-friendly interface where users can see the incoming data in real-time. This could be done using libraries like Plotly or Dash for interactive visualizations.
3. **Anomaly Detection**: Utilize the 'armor-mcp' package to process the incoming data streams and identify any anomalies. 'armor-mcp' provides advanced data observability tools specifically tailored for AI assistants, which you will leverage here to detect unusual patterns or outliers.
4. **Alert System**: Integrate an alert system that notifies users via email or SMS when anomalies are detected. This ensures that any significant deviations from normal behavior are brought to the attention of relevant stakeholders immediately.
5. **Customizable Thresholds**: Allow users to set customizable thresholds for anomaly detection based on their specific needs and requirements.
6. **Historical Data Analysis**: Include functionality to analyze historical data, providing insights into past anomalies and trends over time.
7. **User Authentication**: Implement basic user authentication to ensure only authorized personnel have access to the dashboard.
8. **Documentation**: Provide comprehensive documentation explaining how to install and use the application, including setup instructions and examples.

Your final product should be a robust, scalable, and easy-to-use tool for monitoring and managing data streams in real-time, capable of identifying potential issues before they become major problems.

💬 Discussion Feed

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