aemo-mcp

v0.4.16 safe
3.0
Low Risk

MCP server for the Australian Energy Market Operator (AEMO) NEM. Plain-English access to 5-min dispatch prices, regional demand, interconnector flows, generation by fuel, and rooftop PV.

🤖 AI Analysis

Final verdict: SAFE

The package shows minimal risk indicators, with no evidence of malicious activities such as shell execution, obfuscation, or credential harvesting. The main concern lies in the metadata risk due to the maintainer's limited activity and the missing repository.

  • Network risk present but legitimate
  • No shell execution detected
  • No obfuscation detected
  • No credential harvesting detected
  • Metadata risk due to new maintainer and missing repository
Per-check LLM notes
  • Network: The presence of network calls is common for packages that need to fetch remote data or interact with APIs, but further investigation is needed to ensure the legitimacy of the API endpoints.
  • Shell: No shell execution patterns were detected, which is positive and indicates lower risk.
  • Obfuscation: No obfuscation patterns detected, indicating low risk.
  • Credentials: No credential harvesting patterns detected, indicating low risk.
  • Metadata: The repository is not found and the maintainer has only one package, which may indicate a less experienced or new user. No clear signs of typosquatting or malicious intent.

🔬 Heuristic Checks

⚠ Outbound Network Calls score 1.5

Found 1 network call pattern(s)

  • mo-mcp)" self._http = httpx.AsyncClient( timeout=DEFAULT_TIMEOUT, transport=
✓ 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 score 2.0

Found 1 suspicious link(s) on the package page

  • Non-HTTPS external link: http://nemweb.com.au/Reports/Current/
⚠ 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 aemo-mcp 
Create a Python-based web application that visualizes real-time data from the Australian Energy Market Operator (AEMO) using the 'aemo-mcp' package. This application will provide users with interactive graphs and charts to analyze key metrics such as 5-minute dispatch prices, regional demand, interconnector flows, generation by fuel type, and rooftop photovoltaic (PV) installations. The app should allow users to select different time periods and regions of interest to tailor their analysis.

Steps to Build the Application:
1. Set up a virtual environment and install necessary packages including Flask for the web framework, Matplotlib for plotting, and 'aemo-mcp' for accessing AEMO data.
2. Design the database schema to store fetched data efficiently. Consider using SQLite for simplicity or PostgreSQL for larger datasets.
3. Implement API endpoints using Flask to fetch and process data from 'aemo-mcp'. These endpoints should support querying by date range and region.
4. Create HTML templates and use Jinja2 templating engine to display fetched data on the frontend. Include dropdown menus for selecting regions and time periods.
5. Integrate JavaScript libraries like D3.js or Chart.js for dynamic and interactive data visualization on the webpage.
6. Add error handling and logging mechanisms to ensure the application runs smoothly even when faced with unexpected issues.
7. Test the application thoroughly to ensure all features work as expected and make adjustments based on feedback.

Suggested Features:
- Interactive line graphs showing changes in dispatch prices over selected time periods.
- Bar charts displaying regional demand at various points in time.
- Pie charts illustrating the distribution of energy generation across different fuel types.
- Maps visualizing interconnector flows between regions.
- Comparison tools allowing users to overlay multiple data series for side-by-side analysis.

Utilization of 'aemo-mcp':
- Use 'aemo-mcp' to fetch raw data directly from AEMO's servers without needing to manually download files or deal with complex file formats.
- Leverage 'aemo-mcp's ability to parse and organize data into usable formats, making it easier to integrate with other Python libraries for data manipulation and visualization.
- Take advantage of 'aemo-mcp's plain-English interface to simplify the codebase and improve maintainability.