🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows low risks in terms of network calls, shell execution, obfuscation, and credential harvesting. However, the metadata risk score is elevated due to low repository activity and sparse maintainer information, suggesting potential suspicious behavior.

Elevated metadata risk due to low repository activity and sparse maintainer information
Otherwise, low individual risk scores across other categories

Per-check LLM notes

Network: The network call to localhost suggests internal health checking which is generally benign.
Shell: No shell execution patterns detected.
Obfuscation: No obfuscation patterns detected, indicating low risk.
Credentials: No credential harvesting patterns detected, indicating low risk.
Metadata: The repository has low activity and the maintainer's information is sparse, raising suspicion but not definitive evidence of malice.

📦 Package Quality Overall: Low (4.8/10)

◈ Medium Test Suite 6.0

Partial test coverage signals detected

1 test file(s) detected (e.g. test_server.py)

◈ Medium Documentation 5.0

Some documentation present

Detailed PyPI description (4005 chars)

○ Low Contributing Guide 2.0

No contributing guide or governance files found

No CONTRIBUTING, CODE_OF_CONDUCT, or governance files found

◈ Medium Type Annotations 5.0

Partial type annotation coverage

18 type-annotated function signatures detected in source

◈ Medium Multiple Contributors 6.0

Limited contributor diversity

2 unique contributor(s) across 42 commits in CSOAI-ORG/agriculture-robotics-mcp
Two distinct contributors found

🔬 Heuristic Checks

⚠ Outbound Network Calls score 1.5

Found 1 network call pattern(s)

try: resp = urllib.request.urlopen("http://localhost:8000/health", timeout=2)

✓ 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: meok.ai>

✓ Suspicious Page Links

All external links appear legitimate

⚠ Git Repository History score 2.5

Git history flags: Repository has zero stars and zero forks

Repository has zero stars and zero forks

⚠ 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 agriculture-robotics-mcp

Develop a mini-application named 'Agrifarm Guardian' that leverages the capabilities of the 'agriculture-robotics-mcp' package to assist farmers in optimizing their agricultural operations. This application will serve as a digital assistant that integrates various functionalities to enhance farm efficiency and sustainability. Here’s a detailed breakdown of what your application should accomplish:

1. **Safety Check Module**: Implement a feature where users can input details about their robotic equipment (e.g., model, serial number). Utilize the 'robot safety check' capability from the package to perform a comprehensive safety assessment and generate a report indicating any potential risks or maintenance needs.
2. **Spray Plan Calculator**: Enable users to upload information about their crops and the area they wish to treat. Use the 'spray plan calculator' feature to determine the optimal amount of pesticide or fertilizer needed, along with a suggested schedule based on crop type and environmental conditions.
3. **Harvest Optimization Tool**: Allow farmers to enter data such as crop growth stages, expected yield, and labor availability. The 'harvest optimization' function will then suggest the best time for harvesting to maximize yield and minimize losses due to weather or pest infestations.
4. **User Interface**: Design a user-friendly interface that allows easy navigation between different modules. Include visual aids like charts and graphs to help users understand the outputs from each tool.
5. **Integration and Reporting**: Ensure all modules integrate seamlessly and provide a consolidated report at the end of each operation. This report should summarize key findings and recommendations, helping farmers make informed decisions.

To utilize the 'agriculture-robotics-mcp' package effectively, follow these steps:
- Import the necessary functions from the package into your application code.
- For each module, map user inputs directly to the corresponding functions within the package.
- Handle outputs appropriately, translating technical data into actionable insights through your application’s UI.

By building 'Agrifarm Guardian', you will not only demonstrate proficiency in integrating advanced AI tools but also contribute to sustainable farming practices and increased productivity.