🤖 AI Analysis

Final verdict: SAFE

The package shows no signs of malicious activity such as network calls, shell executions, obfuscation, or credential harvesting. However, the metadata risk score is slightly elevated due to the author's new account and lack of PyPI classifiers.

No network calls detected
No shell execution patterns
Low obfuscation risk
No credential harvesting patterns
Metadata risk due to new author account

Per-check LLM notes

Network: No network calls detected, which is not necessarily suspicious and depends on the package's intended functionality.
Shell: No shell execution patterns detected, indicating the package does not execute external commands directly.
Obfuscation: No obfuscation patterns detected, indicating low risk of malicious intent.
Credentials: No credential harvesting patterns detected, indicating safe handling of secrets.
Metadata: Low risk due to lack of red flags, but author's new account and absence of PyPI classifiers suggest low engagement or oversight.

🔬 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

No author email provided

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository yaogdu/AgentLedger appears legitimate

⚠ Maintainer History score 4.0

2 maintainer concern(s) found

Author "AgentLedger Contributors" appears to have only 1 package on PyPI (new or inactive account)
Package has no PyPI classifiers (low effort / metadata quality)

✓ Known CVE Vulnerabilities

No known vulnerabilities found in OSV database.

💡 AI App Starter Prompt

Use this prompt to build a project with agentledger-mcp

Create a mini-application that serves as a personal financial tracker using the 'agentledger-mcp' package. This application will allow users to log their daily expenses and income, categorize transactions, and generate reports on their financial health over time. The application should include the following features:

1. User Authentication: Implement a simple user authentication system to allow multiple users to track their finances independently.
2. Transaction Logging: Users should be able to log daily transactions, including date, amount, category (e.g., groceries, entertainment), and a brief description.
3. Category Management: Allow users to add, remove, and edit transaction categories.
4. Reporting: Provide basic reporting functionality, such as total spending per month, average spending per category, and savings trends over time.
5. Backup and Sync: Ensure that the application can sync data across devices and provide a backup feature for user data.

To utilize the 'agentledger-mcp' package, you'll need to set up a context adapter within your application that integrates with the MCP tool provided by the package. This will enable the application to manage ledgers efficiently, ensuring that all transactions are recorded accurately and securely. Additionally, leverage the package's capabilities to enhance the security of user data and improve the overall performance of the application.