🤖 AI Analysis

Final verdict: SAFE

The package shows minimal risks across all categories except for metadata, where there is some concern due to sparse maintainer information and lack of community engagement.

Network risk is moderate due to network requests.
Metadata risk is elevated due to sparse maintainer information and low community engagement.

Per-check LLM notes

Network: The use of httpx.AsyncClient suggests the package performs network requests, which is common but requires scrutiny to ensure it's not misused.
Shell: No shell execution patterns detected, indicating low risk.
Obfuscation: No obfuscation patterns detected, indicating low risk of malicious obfuscation.
Credentials: No credential harvesting patterns detected, suggesting no risk of secret theft.
Metadata: The maintainer's information is sparse, and the repository lacks community engagement, raising some concerns.

🔬 Heuristic Checks

⚠ Outbound Network Calls score 1.5

Found 1 network call pattern(s)

httpx.AsyncClient: return httpx.AsyncClient( timeout=DEFAULT_TIMEOUT, headers={"User-Agent": _us

✓ 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: hotmail.com>

✓ 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-tools-mcp

Create a command-line utility called 'MCP Explorer' that leverages the 'agent-tools-mcp' Python package to discover and interact with various services and agents within an MCP (Multi-Cloud Platform) environment. This utility should allow users to:

1. List all available x402 paid services on their selected MCP server.
2. Retrieve detailed information about specific A2A (Agent-to-Agent) agents, including their capabilities and status.
3. Query and display information about MCP servers, such as their geographical location and load capacity.
4. Optionally, allow users to initiate simple interactions with discovered agents, like sending basic commands or requests.

The application should start by prompting the user to input their MCP agent credentials and selecting an MCP server. After logging in, the main menu will present options to perform the above tasks. Each action should provide clear feedback and error handling for common issues like invalid inputs or service unavailability.

Utilize the 'agent-tools-mcp' package to handle all communication with MCP servers and agents. Ensure that your implementation includes proper documentation, comments, and follows best practices for Python coding and security.