awslabs.timestream-for-influxdb-mcp-server

v0.0.18 safe
2.0
Low Risk

An AWS Labs Model Context Protocol (MCP) server for Timestream for InfluxDB

🤖 AI Analysis

Final verdict: SAFE

The package appears safe based on the analysis notes. There are no indications of malicious activities such as network calls, shell executions, obfuscations, or credential risks.

  • Low network risk
  • Very low shell risk
Per-check LLM notes
  • Network: Low risk as no network calls are detected, which might be unusual but not necessarily indicative of malicious activity without further context.
  • Shell: Very low risk as no shell execution patterns are detected.
  • Obfuscation: The observed pattern is commonly used for extending package paths and is not indicative of malicious activity.
  • Credentials: No patterns indicative of credential harvesting were detected.
  • Metadata: The maintainer has only one package, which may indicate a new or less active account but does not strongly suggest malicious intent.

📦 Package Quality Overall: Medium (5.8/10)

✦ High Test Suite 9.0

Test suite present — 3 test file(s) found

  • Test runner config found: pyproject.toml
  • 3 test file(s) detected (e.g. test_init.py)
◈ Medium Documentation 7.0

Some documentation present

  • Documentation URL: "docs" -> https://awslabs.github.io/mcp/servers/timestream-for-influxd
  • Detailed PyPI description (6340 chars)
○ Low Contributing Guide 2.0

No contributing guide or governance files found

  • No CONTRIBUTING, CODE_OF_CONDUCT, or governance files found
○ Low Type Annotations 1.0

No type annotations detected

  • No type annotations, py.typed marker, or stub files detected
✦ High Multiple Contributors 10.0

Active multi-contributor project

  • 42 unique contributor(s) across 100 commits in awslabs/mcp
  • Active community — 5 or more distinct contributors

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

⚠ Code Obfuscation score 2.0

Found 1 obfuscation pattern(s)

  • amespace packages. __path__ = __import__('pkgutil').extend_path(__path__, __name__) # Copyright Amazon.com, In
✓ 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: amazon.com>

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository awslabs/mcp appears legitimate

⚠ Maintainer History score 2.0

1 maintainer concern(s) found

  • Author "Amazon Web Services" 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 awslabs.timestream-for-influxdb-mcp-server 
Create a real-time data visualization dashboard that integrates with AWS Timestream using the 'awslabs.timestream-for-influxdb-mcp-server' package. This dashboard will allow users to monitor and visualize time-series data in real-time, which could be useful for tracking metrics such as server performance, IoT device readings, or financial market trends.

### Project Scope:
1. **Setup**: Install the necessary packages including 'awslabs.timestream-for-influxdb-mcp-server', Flask for web development, and Plotly for visualization.
2. **Data Collection**: Use 'awslabs.timestream-for-influxdb-mcp-server' to connect to AWS Timestream and stream data into the MCP server. Ensure the server is configured correctly to accept data from various sources.
3. **Real-Time Data Processing**: Implement real-time data processing logic to filter, aggregate, and analyze incoming data streams. Use Python libraries like Pandas for efficient data manipulation.
4. **Web Dashboard**: Develop a user-friendly web interface using Flask. The dashboard should display live graphs and charts using Plotly, reflecting the current state of the data being collected.
5. **User Authentication**: Integrate basic user authentication to ensure only authorized users can access the dashboard. Use Flask-Security for managing user roles and permissions.
6. **Customizable Dashboards**: Allow users to customize their dashboards by selecting which data streams they want to monitor and how they want the data to be visualized.
7. **Alerts and Notifications**: Implement alert systems that notify users via email or SMS when certain thresholds are met or exceeded based on the data trends.
8. **Documentation**: Provide comprehensive documentation detailing how to set up and use the dashboard, including setup instructions, configuration options, and troubleshooting tips.

### Utilization of 'awslabs.timestream-for-influxdb-mcp-server':
- Configure the MCP server to act as a bridge between your data sources and AWS Timestream. Ensure it can handle different types of time-series data efficiently.
- Use the server's capabilities to ingest and store data directly into Timestream without needing additional middleware.
- Leverage the server's API to query data from Timestream in real-time for displaying on the dashboard.

This project aims to demonstrate the power of integrating AWS services with custom applications for real-world use cases involving time-series data analysis.

💬 Discussion Feed

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