aihydro-tools

v1.7.0 suspicious
5.0
Medium Risk

AI-Hydro: hydrological research tools as an MCP server for AI agents

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package has moderate risks due to potential misuse of network calls and shell executions. While there are no clear signs of malicious intent, the incomplete metadata and possible misuse of services warrant caution.

  • High shell risk due to potential for arbitrary command execution
  • Moderate network risk due to interactions with Google Cloud APIs
Per-check LLM notes
  • Network: The network calls to Google Cloud APIs seem related to legitimate service interactions but could be used for unauthorized access if credentials are compromised.
  • Shell: Executing shell commands can be risky as it allows arbitrary command execution which might lead to privilege escalation or other malicious activities.
  • Obfuscation: No obfuscation patterns detected, indicating low risk.
  • Credentials: No credential harvesting patterns detected, indicating low risk.
  • Metadata: The maintainer's author information is incomplete and they seem to be new or inactive, which raises some concerns but does not strongly indicate malicious intent.

📦 Package Quality Overall: Medium (5.4/10)

◈ Medium Test Suite 6.0

Partial test coverage signals detected

  • Test runner config found: pyproject.toml
◈ Medium Documentation 7.0

Some documentation present

  • Documentation URL: "Documentation" -> https://github.com/AI-Hydro/AI-Hydro/wiki
  • Detailed PyPI description (20785 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

  • 248 type-annotated function signatures detected in source
◈ Medium Multiple Contributors 5.0

Limited contributor diversity

  • 1 unique contributor(s) across 100 commits in AI-Hydro/AI-Hydro
  • Single author but highly active (100 commits)

🔬 Heuristic Checks

⚠ Outbound Network Calls score 3.0

Found 2 network call pattern(s)

  • loading %s", url) urllib.request.urlretrieve(url, dest) # noqa: S310 if dest.suf
  • Request()) response = requests.get( "https://cloudresourcemanager.googleapis.com/v1
✓ Code Obfuscation

No obfuscation patterns detected

⚠ Shell / Subprocess Execution score 8.0

Found 4 shell execution pattern(s)

  • except ImportError: subprocess.check_call([sys.executable, "-m", "pip", "install", "gdown", "-q"]) d
  • s(): try: subprocess.check_call( [ "git",
  • = [] try: proc = subprocess.run( ["gcloud", "projects", "list", "--format=json(p
  • = [] try: proc = subprocess.run( ["earthengine", "set_project", pid],
✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

Email domain looks legitimate: purdue.edu>

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository AI-Hydro/AI-Hydro 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 aihydro-tools 
Create a fully-functional mini-app called 'HydroAI Monitor' using the Python package 'aihydro-tools'. This app will serve as a monitoring tool for hydrological data, leveraging the capabilities of AI agents hosted on the MCP server provided by 'aihydro-tools'. The goal is to develop a user-friendly interface where users can input real-time or historical hydrological data, such as river flow rates, precipitation levels, and soil moisture content, and receive predictive analytics and insights from AI-driven models.

Key Features:
1. Data Input Interface: Users should be able to upload CSV files containing hydrological data or enter data manually through a form-based UI.
2. Real-Time Data Integration: The app should connect to live data feeds from various hydrological stations to fetch current data.
3. Predictive Analytics: Utilize AI models within 'aihydro-tools' to predict future trends based on historical and real-time data.
4. Visualization Dashboard: Implement interactive charts and graphs using libraries like Matplotlib or Plotly to display the data trends and predictions.
5. Alerts and Notifications: Set up alerts for critical conditions, such as extreme flow rates or drought warnings, and notify users via email or SMS.
6. User Management: Include basic user authentication and role-based access control to manage different user types, such as administrators, analysts, and general users.

Steps to Develop:
1. Setup Project Environment: Initialize a new Python project and install necessary dependencies, including 'aihydro-tools'.
2. Design User Interface: Create a simple yet effective web interface using Flask or Django for data entry and visualization.
3. Integrate Data Sources: Connect the app to external data sources for real-time data acquisition.
4. Implement AI Models: Use 'aihydro-tools' to deploy and run AI models for predictive analysis.
5. Develop Visualization Components: Build visualizations to represent the data and predictions effectively.
6. Configure Alerts: Set up alert mechanisms based on thresholds defined by users or predefined critical values.
7. Test and Deploy: Thoroughly test all functionalities and deploy the application on a server or cloud platform.

How 'aihydro-tools' is Utilized:
- For deploying and managing AI agents that perform predictive analytics.
- To facilitate the connection between the app and external data sources through its MCP server capabilities.
- For providing pre-built AI models tailored for hydrological data analysis.

This project aims to demonstrate the practical application of AI in environmental monitoring and management, making it easier for researchers, policymakers, and stakeholders to make informed decisions based on accurate and timely data.

💬 Discussion Feed

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