🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows signs of obfuscation and has a new or inactive maintainer's PyPI account with an unavailable git repository, raising suspicion about its legitimacy.

High obfuscation risk
Inactive or new maintainer's PyPI account
Unavailability of git repository

Per-check LLM notes

Network: The use of httpx.AsyncClient indicates the package makes network requests, which could be benign if it's designed to interact with APIs or fetch external resources.
Shell: No shell execution patterns detected, suggesting no immediate risk from executing system commands.
Obfuscation: The observed patterns suggest potential obfuscation to hide the import and function calls, which is suspicious but could also be part of legitimate code obfuscation practices.
Credentials: No clear signs of credential harvesting or secret handling are present in the provided code snippets.
Metadata: The maintainer has a new or inactive PyPI account and the git repository is not available, which raises some concerns but does not definitively indicate malicious intent.

🔬 Heuristic Checks

⚠ Outbound Network Calls score 6.0

Found 4 network call pattern(s)

Cache() self._http = httpx.AsyncClient( timeout=DEFAULT_TIMEOUT, transport=
tream broke") async with httpx.AsyncClient(transport=httpx.MockTransport(handler)) as http: # F
lendar", ) async with httpx.AsyncClient() as http: entries_30, _, _ = await fetch_release_ca
lendar", ) async with httpx.AsyncClient() as http: entries, _, _ = await fetch_release_calen

⚠ Code Obfuscation score 4.0

Found 2 obfuscation pattern(s)

"n": 0} real_read_sdmx = __import__("sdmx").read_sdmx def counting_read_sdmx(*args, **kwargs): #
{"n": 0} real_read_sdmx = __import__("sdmx").read_sdmx def counting(*args, **kwargs): # noqa: ANN0

✓ 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 score 3.0

Repository not found (deleted or private)

Repository not found (deleted or private)

⚠ Maintainer History score 2.0

1 maintainer concern(s) found

Author "Harry Vass" 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 abs-mcp

Create a Python-based mini-application that leverages the 'abs-mcp' package to provide users with easy access to curated economic data from the Australian Bureau of Statistics (ABS). Your application should allow users to query and visualize key economic indicators such as Labour Force statistics, Consumer Price Index (CPI), Estimated Resident Population (ERP), Building Approvals, and Lending Indicators. The app should have the following core functionalities:

1. User-friendly command line interface for querying data.
2. Ability to select specific time periods for the data retrieval.
3. Integration with a simple visualization library (e.g., matplotlib) to display the queried data graphically.
4. Option to save the retrieved data into a CSV file for further analysis.
5. Provide short descriptions of each dataset to help users understand the context of the data they are accessing.
6. Error handling for invalid inputs or connection issues with the ABS Data API.

Your task is to design and implement this application using the 'abs-mcp' package as the primary tool for interfacing with the ABS data API. Ensure that your code is well-documented and includes comments explaining how the 'abs-mcp' package is utilized at various stages of the application. Additionally, create a README.md file that outlines the installation process, usage instructions, and any dependencies required to run the application.