🤖 AI Analysis

Final verdict: SUSPICIOUS

The package has no apparent direct security risks like network calls, shell execution, or obfuscation. However, its metadata raises concern due to the lack of maintainer history and a missing GitHub repository, suggesting potential unreliability or malicious intent.

Minimal maintainer history
Lack of a GitHub repository

Per-check LLM notes

Network: No network calls detected, which is typical for many packages but could be unusual if the package's functionality requires external communication.
Shell: No shell execution patterns detected, indicating that the package does not execute system commands which reduces the risk of unauthorized access or command execution.
Obfuscation: No obfuscation patterns detected, indicating low risk of malicious activity.
Credentials: No credential harvesting patterns detected, indicating safe handling of sensitive information.
Metadata: The package is newly uploaded with minimal maintainer history and lacks a GitHub repository, raising suspicion.

🔬 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

Email domain looks legitimate: gmail.com>

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

No GitHub repository linked

No GitHub repository link found

⚠ Maintainer History score 8.0

4 maintainer concern(s) found

Only one version has ever been released — brand new package
Package uploaded less than 24 hours ago (2026-06-05T09:58:38.000Z)
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 ado-team-activity-mcp

Create a Python-based dashboard application that integrates with the ado-team-activity-mcp package to provide managers with insights into their team's productivity and task completion over various time periods. This tool will allow managers to query specific team members' activities, track progress on projects, and analyze overall team performance. Here are the steps and features you should consider implementing:

1. **Setup and Configuration**: Begin by installing the ado-team-activity-mcp package. Configure your application to connect to an Azure DevOps organization and select the appropriate project and team. Ensure that the application can handle authentication securely.

2. **User Interface**: Develop a user-friendly interface where managers can input queries such as 'What has John Doe worked on in the past month?' or 'How many hours did Jane Smith spend on Feature X?' The UI should also allow for filtering results based on dates, team members, and types of work items (e.g., bugs, tasks, epics).

3. **Query Processing**: Utilize the ado-team-activity-mcp package to process these queries. The package should fetch relevant data from Azure DevOps work items, including details about tasks, bugs, and other work items assigned to team members.

4. **Data Visualization**: Implement visual representations of the queried data, such as pie charts showing the distribution of work item types, bar graphs indicating the number of hours spent on different tasks, and timelines illustrating the progression of tasks over time.

5. **Analysis Tools**: Provide tools for deeper analysis, such as trend analysis over a six-month period, identifying the busiest weeks or months, and comparing individual contributions to team goals.

6. **Reporting**: Allow users to generate reports based on the analyzed data, which can be exported in formats like PDF or Excel for sharing or archiving.

7. **Security and Compliance**: Ensure that all data handling complies with privacy policies and security standards. Implement measures to protect sensitive information.

8. **Testing and Validation**: Conduct thorough testing to ensure accuracy and reliability of the data presented. Validate the app against real-world scenarios to identify and fix any issues.

By following these steps and incorporating these features, your application will become a valuable tool for managers looking to gain deeper insights into their team's performance and productivity using the ado-team-activity-mcp package.