alpaca-mcp-server

v2.0.2 safe
3.0
Low Risk

Alpaca Trading API integration for Model Context Protocol (MCP)

🤖 AI Analysis

Final verdict: SAFE

The package appears safe with minimal risks identified across various checks. The network, shell, obfuscation, and credential risks are all low.

  • Low risk scores across network, shell, obfuscation, and credential checks.
  • Metadata risk slightly elevated due to sparse author information.
Per-check LLM notes
  • Network: Network calls to trading and data clients seem to be part of normal operations for a server handling financial data.
  • Shell: No shell execution patterns detected, indicating no immediate risk from command execution.
  • Obfuscation: No obfuscation patterns detected, indicating low risk of malicious intent.
  • Credentials: No credential harvesting patterns detected, indicating secure handling of sensitive information.
  • Metadata: The author's information is sparse, indicating potential lack of transparency or newness.

📦 Package Quality Overall: Medium (6.4/10)

◈ Medium Test Suite 6.0

Partial test coverage signals detected

  • Test runner config found: pyproject.toml
◈ Medium Documentation 7.0

Some documentation present

  • Documentation URL: "Documentation" -> https://github.com/alpacahq/alpaca-mcp-server#readme
  • Detailed PyPI description (28684 chars)
○ Low Contributing Guide 4.0

No contributing guide or governance files found

  • Development Status classifier >= Beta
◈ Medium Type Annotations 5.0

Partial type annotation coverage

  • 26 type-annotated function signatures detected in source
✦ High Multiple Contributors 10.0

Active multi-contributor project

  • 14 unique contributor(s) across 100 commits in alpacahq/alpaca-mcp-server
  • Active community — 5 or more distinct contributors

🔬 Heuristic Checks

⚠ Outbound Network Calls score 3.0

Found 2 network call pattern(s)

  • ops: trading_client = httpx.AsyncClient( base_url=trading_base, headers=auth
  • ec_ops: data_client = httpx.AsyncClient( base_url=data_base, headers=auth_he
✓ 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: alpaca.markets>

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository alpacahq/alpaca-mcp-server appears legitimate

⚠ 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 alpaca-mcp-server 
Create a real-time stock trading simulation application using the 'alpaca-mcp-server' Python package. This application will allow users to simulate trading stocks based on live market data from Alpaca Trading API, utilizing the Model Context Protocol (MCP) for enhanced context and analytics. Here's a step-by-step guide to building this mini-app:

1. **Setup**: Install the necessary packages including 'alpaca-mcp-server', 'requests', and 'flask'. Ensure you have your Alpaca API keys ready.
2. **API Integration**: Use 'alpaca-mcp-server' to integrate with the Alpaca Trading API. Set up endpoints to fetch live market data, place orders, and retrieve account information.
3. **User Interface**: Develop a simple Flask web application that allows users to view current market prices, submit buy/sell orders, and see their simulated portfolio performance.
4. **Real-Time Data Feeds**: Implement real-time data feeds using MCP to keep the application updated with the latest market conditions.
5. **Analytics and Insights**: Provide basic analytics like historical price charts and trend analysis. Use MCP to enhance these insights by integrating additional context from external sources.
6. **Security Measures**: Ensure all user interactions are secure by implementing proper authentication and data encryption.
7. **Testing and Deployment**: Test the application thoroughly and deploy it on a cloud service like AWS or Heroku.

Suggested Features:
- Real-time stock price updates
- Order history and transaction records
- Portfolio overview with gains/losses
- Historical price chart visualization
- Basic technical analysis tools
- User authentication and security measures

The 'alpaca-mcp-server' package will be the backbone of your application, enabling seamless integration with Alpaca's powerful trading API while enhancing the application's functionality through MCP's contextual capabilities.