🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows some signs of potential misuse due to minimal maintainer activity and an inactive repository, despite having low risks in terms of network, shell, obfuscation, and credential handling.

Minimal maintainer history
Inactive repository

Per-check LLM notes

Network: The network call to localhost suggests the package might be performing local health checks, which is generally benign but could indicate unexpected behavior if not documented.
Shell: No shell execution patterns were detected, indicating low risk.
Obfuscation: No obfuscation patterns detected, indicating low risk.
Credentials: No credential harvesting patterns detected, indicating low risk.
Metadata: The package has minimal maintainer history and an inactive repository, raising concerns about its legitimacy.

🔬 Heuristic Checks

⚠ Outbound Network Calls score 1.5

Found 1 network call pattern(s)

try: resp = urllib.request.urlopen("http://localhost:8000/health", timeout=2)

✓ 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: meok.ai>

✓ Suspicious Page Links

All external links appear legitimate

⚠ Git Repository History score 2.5

Git history flags: Repository has zero stars and zero forks

Repository has zero stars and zero forks

⚠ Maintainer History score 4.0

2 maintainer concern(s) found

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 agent-negotiation-mcp

Create a fully functional mini-application called 'Negotiator' that leverages the 'agent-negotiation-mcp' package from MEOK AI Labs to simulate negotiations between two parties. The application should allow users to input initial conditions for a negotiation scenario, such as the items being negotiated over and the starting offers from each party. The core functionality of 'Negotiator' will involve proposing deals, evaluating offers, and making counter-offers until a final agreement is reached or the negotiation fails. Here’s a step-by-step guide on building this application:

1. **Setup**: Install the necessary Python packages including 'agent-negotiation-mcp'. Ensure your development environment is set up properly.
2. **Design User Interface**: Develop a simple and intuitive interface where users can enter details about the negotiation scenario. This could be a web-based interface or a command-line tool, depending on your preference.
3. **Initialize Negotiation Scenario**: Users should be able to specify the items involved in the negotiation and their respective values or importance to each party. This sets the stage for the negotiation process.
4. **Propose Deals**: Use the 'propose deal' feature of the 'agent-negotiation-mcp' package to generate initial proposals based on the user-defined scenario. These proposals should reflect the interests and priorities of both parties.
5. **Evaluate Offers**: Implement a mechanism to evaluate these initial proposals using the 'evaluate offer' functionality provided by the package. This evaluation should consider factors like fairness, balance of power, and alignment with each party's objectives.
6. **Counter Offers**: Allow the application to make intelligent counter-offers through the 'counter offer' feature of 'agent-negotiation-mcp'. This should refine the proposals towards a mutually acceptable agreement.
7. **Final Agreement or Failure**: Based on the outcomes of the evaluations and counter-offers, the application should either reach a final agreement or determine that the negotiation has failed.
8. **Reporting**: Provide a summary report of the negotiation process, detailing each proposal, evaluation, and counter-offer made throughout the process. This report should help users understand the dynamics of the negotiation and the reasoning behind the final outcome.

Suggested additional features include allowing users to customize the negotiation parameters, such as the degree of competitiveness or cooperativeness of the agents, and providing visual aids like charts or graphs to illustrate the negotiation progress.