sellsyde-mcp

v1.0.0 suspicious
4.0
Medium Risk

Official Model Context Protocol (MCP) server for SellSyde.ai

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows potential for network communication, but lacks critical metadata such as maintainer history and author details, which raises concerns about its legitimacy.

  • network risk due to external communication
  • lack of maintainer history and author details
Per-check LLM notes
  • Network: The presence of network calls suggests the package may communicate with external services, which could be legitimate but requires further investigation to ensure it's not being used for unauthorized data transfer.
  • Shell: No shell execution patterns detected, indicating low risk of direct system command execution from this package.
  • Obfuscation: No obfuscation patterns detected, indicating low risk of malicious intent.
  • Credentials: No credential harvesting patterns detected, suggesting safe handling of secrets and credentials.
  • Metadata: The package is new with no maintainer history and lacks author details, raising suspicion.

🔬 Heuristic Checks

Outbound Network Calls score 1.5

Found 1 network call pattern(s)

  • PI_KEY} async with httpx.AsyncClient() as client: try: response = await cli
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

All external links appear legitimate

Git Repository History

No GitHub repository linked

  • No GitHub repository link found
Maintainer History score 10.0

5 maintainer concern(s) found

  • Only one version has ever been released — brand new package
  • Package uploaded less than 24 hours ago (2026-06-05T00:54:58.000Z)
  • 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)