atlassian-hub

v0.2.0 suspicious
4.0
Medium Risk

The Model Context Protocol (MCP) Atlassian integration is an open-source implementation that bridges Atlassian products (Jira and Confluence) with AI language models following Anthropic's MCP specification. This project enables secure, contextual AI interactions with Atlassian tools while maintaining data privacy and security. Key features include:

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package has low risks associated with network, shell execution, obfuscation, and credential handling. However, its low maintainer activity and lack of standard metadata raise concerns about its reliability and potential for supply-chain attacks.

  • Low maintainer activity
  • Lack of standard metadata
Per-check LLM notes
  • Network: The presence of network calls is expected if the package interacts with Atlassian services, but it should be reviewed to ensure it does not perform unauthorized data exfiltration.
  • Shell: No shell execution patterns detected, which is normal and expected.
  • Obfuscation: No obfuscation patterns detected, indicating low risk of malicious obfuscation.
  • Credentials: No credential harvesting patterns detected, indicating low risk of malicious credential theft.
  • Metadata: The package shows low maintainer activity and lacks standard metadata, indicating potential low effort or inactivity.

📦 Package Quality Overall: Low (4.4/10)

✦ High Test Suite 9.0

Test suite present — 1 test file(s) found

  • Test runner config found: pyproject.toml
  • 1 test file(s) detected (e.g. test_oauth_setup.py)
◈ Medium Documentation 5.0

Some documentation present

  • Detailed PyPI description (7934 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

  • 318 type-annotated function signatures detected in source
○ Low Multiple Contributors 1.0

Unable to verify contributor count: no GitHub repository found

  • No GitHub repository linked — contributor count unavailable

🔬 Heuristic Checks

⚠ Outbound Network Calls score 1.5

Found 1 network call pattern(s)

  • r "") response = requests.get( url, params=params,
✓ 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 6.0

3 maintainer concern(s) found

  • Author name is missing or very short
  • Author "" 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 atlassian-hub 
Create a mini-application called 'ConfluenceAI' that leverages the 'atlassian-hub' package to integrate Jira and Confluence with an AI model following Anthropic's Model Context Protocol (MCP). This application will allow users to interact with their Confluence spaces and Jira projects using natural language queries and receive contextually relevant responses from the AI model.

**Core Features:**
1. **Query Processing:** Users should be able to submit natural language queries related to their Confluence pages and Jira issues. For example, they could ask about the status of a specific issue, request details on a document, or seek information on tasks assigned to them.
2. **Contextual Responses:** The AI should provide detailed, accurate, and contextually relevant responses based on the content within Confluence and Jira. It should be able to understand and interpret complex queries and provide useful insights.
3. **Data Privacy & Security:** Ensure that all interactions are conducted securely and that user data remains private. The application should adhere to Atlassian's data protection guidelines.
4. **User Interface:** Develop a simple, intuitive web-based interface where users can log in with their Atlassian credentials, enter queries, and view responses.
5. **Integration with Atlassian Products:** Use the 'atlassian-hub' package to securely connect with Confluence and Jira APIs, fetching necessary data and passing it to the AI model for processing.
6. **Customization Options:** Allow users to customize the AI's response style, such as formality level, verbosity, etc., to better suit their preferences.
7. **Feedback Mechanism:** Implement a feedback system where users can rate the quality of the AI's responses and suggest improvements.

**Steps to Build the Application:**
1. Set up a development environment with Python and the required libraries, including 'atlassian-hub'.
2. Authenticate users through OAuth to access their Confluence and Jira data.
3. Design a RESTful API that interacts with 'atlassian-hub' to fetch data from Atlassian products.
4. Integrate an AI model that follows the MCP protocol to process user queries and generate responses.
5. Create a frontend using HTML/CSS/JavaScript to provide a seamless user experience.
6. Test the application thoroughly to ensure that it works correctly and securely.
7. Deploy the application on a cloud platform like AWS or Heroku.
8. Gather user feedback and iterate on the design and functionality of the application.

💬 Discussion Feed

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