awslabs.amazon-neptune-mcp-server

v1.0.18 safe
2.0
Low Risk

An Amazon Neptune MCP server that allows for fetching status, schema, and querying using openCypher, Gremlin, and SPARQL for Neptune Database and openCypher for Neptune Analytics.

🤖 AI Analysis

Final verdict: SAFE

The package appears to be safe based on the analysis notes, with low risks across all categories and no indications of malicious activity or supply-chain attacks.

  • Low network and shell risk
  • No evidence of credential harvesting
  • Minimal obfuscation risk
Per-check LLM notes
  • Network: No network calls detected, which is normal if the package does not require external communications.
  • Shell: No shell execution patterns detected, indicating no immediate risk of unauthorized system access.
  • Obfuscation: The observed pattern is commonly used for extending package paths and does not inherently indicate malicious intent.
  • Credentials: No patterns indicative of credential harvesting were found in the provided code snippet.
  • Metadata: The author has only one package, suggesting it might be a new or less active account, but no other red flags were raised.

📦 Package Quality Overall: Medium (6.6/10)

✦ High Test Suite 9.0

Test suite present — 9 test file(s) found

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

Some documentation present

  • Documentation URL: "docs" -> https://awslabs.github.io/mcp/servers/amazon-neptune-mcp-ser
  • Detailed PyPI description (5423 chars)
○ Low Contributing Guide 2.0

No contributing guide or governance files found

  • No CONTRIBUTING, CODE_OF_CONDUCT, or governance files found
◈ Medium Type Annotations 5.0

Partial type annotation coverage

  • 16 type-annotated function signatures detected in source
✦ 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: users.noreply.github.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.amazon-neptune-mcp-server 
Create a fully-functional mini-application called 'NeptuneQueryTool' that leverages the 'awslabs.amazon-neptune-mcp-server' Python package to interact with an Amazon Neptune database. This tool should allow users to perform various operations such as fetching database status, retrieving schema information, and executing queries using openCypher, Gremlin, and SPARQL. Additionally, the application should provide a simple user interface for inputting queries and displaying results. Here are the key steps and features for building this application:

1. **Setup**: Begin by setting up your development environment with the necessary dependencies including 'awslabs.amazon-neptune-mcp-server'. Ensure you have an active Amazon Neptune instance with sample data loaded.
2. **Database Connection**: Implement functionality within the application to connect to the Amazon Neptune database using credentials provided through configuration files or environment variables.
3. **Status Fetching**: Develop a feature to fetch the current status of the Neptune database, including health checks and any ongoing maintenance activities.
4. **Schema Retrieval**: Add capabilities to retrieve the schema information from the database, allowing users to understand the structure of their graph data.
5. **Query Execution**: Enable users to execute different types of queries (openCypher, Gremlin, SPARQL) against the Neptune database directly from the application interface. Provide options for selecting the query language and inputting the query.
6. **Result Display**: Design a user-friendly interface for displaying the results of executed queries, handling both successful responses and error messages gracefully.
7. **User Interface**: Build a simple web-based user interface using Flask or Django where users can input their queries and view the results. The UI should be intuitive and easy to navigate.
8. **Testing & Documentation**: Finally, thoroughly test all functionalities of the application and document them clearly, providing examples on how to use each feature effectively.
By following these steps, you will create a versatile tool that simplifies interaction with Amazon Neptune databases, making it easier for developers and analysts to manage and analyze their graph data.

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